GAGE: generally applicable gene set enrichment for pathway analysis
Background Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. Howe...
Saved in:
| Published in | BMC bioinformatics Vol. 10; no. 1; p. 161 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
27.05.2009
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/1471-2105-10-161 |
Cover
| Abstract | Background
Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.
Results
To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.
GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways–all of which are supported by the experimental literature.
Conclusion
GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from
http://sysbio.engin.umich.edu/~luow/downloads.php
. |
|---|---|
| AbstractList | Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred. GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php. Abstract Background Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. Results To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred. GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways–all of which are supported by the experimental literature. Conclusion GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php. Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature. GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php. Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.BACKGROUNDGene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature.RESULTSTo address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature.GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php.CONCLUSIONGAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php. Background Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. Results To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred. GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways–all of which are supported by the experimental literature. Conclusion GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php . Background Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. Results To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred. GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature. Conclusion GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from |
| ArticleNumber | 161 |
| Audience | Academic |
| Author | Luo, Weijun Friedman, Michael S Hankenson, Kurt D Woolf, Peter J Shedden, Kerby |
| AuthorAffiliation | 2 Bioinformatics Shared Resource, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA 5 Department of Animal Biology, University of Pennsylvania, Philadelphia, PA 19104, USA 7 Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA 6 Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA 1 Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA 3 Thermogenesis Corporation, Rancho Cordova CA, 95742, USA 4 Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA |
| AuthorAffiliation_xml | – name: 2 Bioinformatics Shared Resource, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA – name: 1 Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA – name: 7 Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA – name: 3 Thermogenesis Corporation, Rancho Cordova CA, 95742, USA – name: 6 Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA – name: 4 Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA – name: 5 Department of Animal Biology, University of Pennsylvania, Philadelphia, PA 19104, USA |
| Author_xml | – sequence: 1 givenname: Weijun surname: Luo fullname: Luo, Weijun organization: Department of Biomedical Engineering, University of Michigan, Bioinformatics Shared Resource, Cold Spring Harbor Laboratory – sequence: 2 givenname: Michael S surname: Friedman fullname: Friedman, Michael S organization: Thermogenesis Corporation – sequence: 3 givenname: Kerby surname: Shedden fullname: Shedden, Kerby organization: Department of Statistics, University of Michigan – sequence: 4 givenname: Kurt D surname: Hankenson fullname: Hankenson, Kurt D organization: Department of Animal Biology, University of Pennsylvania – sequence: 5 givenname: Peter J surname: Woolf fullname: Woolf, Peter J email: pwoolf@umich.edu organization: Department of Biomedical Engineering, University of Michigan, Bioinformatics Program, University of Michigan, Department of Chemical Engineering, University of Michigan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19473525$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkt2L1DAUxYusuB_67pMUBMGHrknbJI0PwjCs48CC4MdzSNPbToY2qUnrOv-96XRYt6IifWg5_Z2T3tN7GZ0ZayCKnmN0jXFB3-Cc4STFiCQYJZjiR9HFvXT24Pk8uvR-jxBmBSJPonPMc5aRlFxE681qc_M2bsCAk217iGXft1rJsoWjGHsYYjBOq10HZohr6-JeDrs7GVAj24PX_mn0uJath2en-1X09f3Nl_WH5PbjZrte3SaKUj4khGBCq4yWvCIlkVxRpAqJJSnzlNc0q4BQUhBcs6LM66yqS5WVkjOKkaJpVWZX0XbOrazci97pTrqDsFKLo2BdI6QbtGpBMER5VmQECpXmNUo5h5wzzoCVWS5VGrLwnDWaXh7uwuj3gRiJqVwxtSem9o4KxcHzbvb0Y9lBpUIfobPFhyzfGL0Tjf0uUsppTqZDX50CnP02gh9Ep72CtpUG7OgFZRljPGUBfDmDjQzDaFPbkKcmWKxShAtMUE4Ddf0HKlwVdFqFTal10BeG1wtDYAb4MTRy9F5sP39asi8eDvurndPqBADNgHLWewf1_xRIf7MoPchB26ku3f7LePpbPpxhGnBib0cX1s__3fMTwZbxmw |
| CitedBy_id | crossref_primary_10_1016_j_placenta_2021_02_020 crossref_primary_10_1038_s42003_019_0727_5 crossref_primary_10_1038_s41598_019_46594_x crossref_primary_10_1126_scitranslmed_adi1501 crossref_primary_10_1016_j_cstres_2024_04_006 crossref_primary_10_1021_acsami_3c01099 crossref_primary_10_1177_1099800416629209 crossref_primary_10_3390_cimb47030200 crossref_primary_10_1038_s41598_019_55700_y crossref_primary_10_1126_sciadv_aau9223 crossref_primary_10_1371_journal_pone_0233394 crossref_primary_10_1142_S0219720019400109 crossref_primary_10_3389_fgene_2019_00293 crossref_primary_10_1038_ncomms10043 crossref_primary_10_1093_molbev_msab083 crossref_primary_10_1126_scisignal_aba3244 crossref_primary_10_18632_oncoscience_292 crossref_primary_10_3389_fmicb_2020_00397 crossref_primary_10_3389_fgene_2019_00279 crossref_primary_10_1002_hbm_26512 crossref_primary_10_3390_cancers13194885 crossref_primary_10_1007_s12031_023_02105_2 crossref_primary_10_1016_j_celrep_2020_107887 crossref_primary_10_1111_tpj_13147 crossref_primary_10_1093_bib_bbz090 crossref_primary_10_1038_s43018_020_0078_7 crossref_primary_10_3389_fcell_2022_906240 crossref_primary_10_1101_gr_279517_124 crossref_primary_10_26508_lsa_202000640 crossref_primary_10_3233_JAD_170830 crossref_primary_10_3390_biomedicines12030628 crossref_primary_10_1016_j_ajpath_2014_01_012 crossref_primary_10_1111_apha_14214 crossref_primary_10_3390_insects13040343 crossref_primary_10_1016_j_foodres_2022_111103 crossref_primary_10_1016_j_stemcr_2019_09_011 crossref_primary_10_1038_ncb3476 crossref_primary_10_1002_advs_202301353 crossref_primary_10_1371_journal_pone_0293688 crossref_primary_10_3390_plants10040669 crossref_primary_10_1016_j_imj_2023_02_002 crossref_primary_10_3390_cancers14092227 crossref_primary_10_1016_j_algal_2018_05_004 crossref_primary_10_1038_s41396_021_00998_8 crossref_primary_10_1371_journal_pone_0272769 crossref_primary_10_1007_s00414_025_03414_4 crossref_primary_10_1038_ng_3719 crossref_primary_10_1371_journal_pntd_0007949 crossref_primary_10_1152_ajpendo_00243_2018 crossref_primary_10_1016_j_cmet_2023_12_011 crossref_primary_10_1016_j_modpat_2023_100387 crossref_primary_10_1016_j_neuron_2023_10_023 crossref_primary_10_1016_j_celrep_2023_112627 crossref_primary_10_1093_toxsci_kft230 crossref_primary_10_1242_jcs_121533 crossref_primary_10_1158_2326_6066_CIR_20_1002 crossref_primary_10_1038_s41598_021_98921_w crossref_primary_10_1093_hr_uhae095 crossref_primary_10_1371_journal_pone_0063147 crossref_primary_10_1016_j_jtct_2021_02_017 crossref_primary_10_1111_acel_12386 crossref_primary_10_1016_j_gdata_2015_04_015 crossref_primary_10_1152_ajpgi_00136_2017 crossref_primary_10_1016_j_jcmgh_2020_01_010 crossref_primary_10_3389_fonc_2022_1024567 crossref_primary_10_1016_j_molcel_2011_03_007 crossref_primary_10_1126_sciadv_adg1610 crossref_primary_10_1016_j_jtct_2021_02_023 crossref_primary_10_18632_oncotarget_9297 crossref_primary_10_3390_cells12081195 crossref_primary_10_3389_fimmu_2022_825032 crossref_primary_10_1186_s12967_023_04833_w crossref_primary_10_3389_fimmu_2022_919815 crossref_primary_10_3389_fcvm_2020_616920 crossref_primary_10_1038_s41594_022_00866_9 crossref_primary_10_1128_mSphere_00650_21 crossref_primary_10_1242_dev_160788 crossref_primary_10_1038_s41598_024_54724_3 crossref_primary_10_1016_j_compbiolchem_2018_11_021 crossref_primary_10_1371_journal_pone_0244065 crossref_primary_10_1038_s41598_019_43409_x crossref_primary_10_1016_j_devcel_2021_01_012 crossref_primary_10_1038_s43018_023_00547_6 crossref_primary_10_1002_JLB_5HI0119_018R crossref_primary_10_1038_s41598_020_75855_3 crossref_primary_10_18632_aging_203820 crossref_primary_10_1101_gr_271346_120 crossref_primary_10_1172_JCI132139 crossref_primary_10_1016_j_cmet_2021_01_018 crossref_primary_10_1016_j_celrep_2023_111992 crossref_primary_10_18632_oncotarget_13271 crossref_primary_10_1016_j_matbio_2024_11_005 crossref_primary_10_1093_bioinformatics_btab793 crossref_primary_10_18632_oncotarget_27678 crossref_primary_10_1016_j_isci_2022_104793 crossref_primary_10_3389_fphys_2023_1179288 crossref_primary_10_18632_aging_202500 crossref_primary_10_1016_j_bbapap_2021_140619 crossref_primary_10_1186_s12859_016_1403_0 crossref_primary_10_1016_j_copbio_2014_01_007 crossref_primary_10_18632_oncotarget_13062 crossref_primary_10_1016_j_cyto_2023_156187 crossref_primary_10_1186_s12864_016_2784_1 crossref_primary_10_1038_s41374_020_0408_5 crossref_primary_10_1016_j_bonr_2019_100227 crossref_primary_10_1016_j_ymben_2016_06_008 crossref_primary_10_1073_pnas_1706879114 crossref_primary_10_1038_s41591_018_0323_0 crossref_primary_10_1016_j_gene_2016_11_028 crossref_primary_10_1371_journal_pone_0165543 crossref_primary_10_1186_1471_2164_15_884 crossref_primary_10_1186_s13073_015_0189_4 crossref_primary_10_1002_jor_25251 crossref_primary_10_1016_j_bbacli_2017_07_004 crossref_primary_10_1093_bioinformatics_btx104 crossref_primary_10_3390_biomedicines8090360 crossref_primary_10_1038_s41590_022_01208_z crossref_primary_10_1093_jxb_erv269 crossref_primary_10_1016_j_ocarto_2024_100550 crossref_primary_10_1016_j_celrep_2020_108331 crossref_primary_10_3389_fcimb_2018_00307 crossref_primary_10_1186_s12864_018_5327_0 crossref_primary_10_1152_ajpregu_00074_2019 crossref_primary_10_1111_tpj_13547 crossref_primary_10_1111_1751_7915_12713 crossref_primary_10_1016_j_immuni_2023_01_011 crossref_primary_10_4049_immunohorizons_2100018 crossref_primary_10_3389_fcimb_2022_854242 crossref_primary_10_1186_s12711_016_0200_6 crossref_primary_10_1016_j_molcel_2019_03_040 crossref_primary_10_1186_s12864_018_5207_7 crossref_primary_10_1007_s00428_023_03721_4 crossref_primary_10_1038_s41416_024_02705_8 crossref_primary_10_1186_s13075_021_02477_z crossref_primary_10_1186_s13073_020_00812_8 crossref_primary_10_3389_fmicb_2023_1295160 crossref_primary_10_1158_1541_7786_MCR_20_0275 crossref_primary_10_1182_bloodadvances_2020002804 crossref_primary_10_1038_s41467_019_13883_y crossref_primary_10_4137_CIN_S40301 crossref_primary_10_26508_lsa_201900495 crossref_primary_10_1002_ece3_3602 crossref_primary_10_1007_s00335_015_9570_2 crossref_primary_10_1073_pnas_1614864114 crossref_primary_10_1371_journal_pone_0281645 crossref_primary_10_1158_1940_6207_CAPR_20_0053 crossref_primary_10_1371_journal_ppat_1006348 crossref_primary_10_4137_CIN_S14066 crossref_primary_10_1126_sciadv_abb3461 crossref_primary_10_1242_dev_159103 crossref_primary_10_1093_nar_gkab447 crossref_primary_10_3390_cells12030422 crossref_primary_10_1093_nar_gkab204 crossref_primary_10_1186_s12859_019_2870_x crossref_primary_10_1038_s41418_019_0465_8 crossref_primary_10_1186_s12866_023_02942_6 crossref_primary_10_3389_fmicb_2021_760017 crossref_primary_10_1371_journal_pbio_3000178 crossref_primary_10_1186_s13059_018_1442_0 crossref_primary_10_3389_fimmu_2022_1057421 crossref_primary_10_7717_peerj_8684 crossref_primary_10_1093_ismejo_wraf013 crossref_primary_10_1186_s13395_017_0144_8 crossref_primary_10_1136_gutjnl_2021_324295 crossref_primary_10_3389_fgene_2024_1249751 crossref_primary_10_3389_fcimb_2021_605825 crossref_primary_10_1016_j_fm_2024_104617 crossref_primary_10_1016_j_heliyon_2023_e23220 crossref_primary_10_1152_ajpcell_00218_2021 crossref_primary_10_1186_s13073_022_01056_4 crossref_primary_10_1080_2162402X_2020_1731135 crossref_primary_10_1109_TCBB_2019_2907246 crossref_primary_10_1093_nar_gks461 crossref_primary_10_1016_j_isci_2021_103596 crossref_primary_10_1136_gutjnl_2017_314549 crossref_primary_10_1371_journal_pbio_3001062 crossref_primary_10_7554_eLife_14845 crossref_primary_10_1161_JAHA_119_012919 crossref_primary_10_1016_j_micpath_2022_105560 crossref_primary_10_1016_j_canlet_2018_01_082 crossref_primary_10_1172_jci_insight_90299 crossref_primary_10_1016_j_celrep_2019_05_091 crossref_primary_10_1172_jci_insight_130651 crossref_primary_10_1002_jor_25296 crossref_primary_10_3390_biology12081066 crossref_primary_10_18632_oncotarget_2820 crossref_primary_10_1016_j_xjidi_2023_100251 crossref_primary_10_1164_rccm_202305_0793OC crossref_primary_10_1002_mgg3_207 crossref_primary_10_1186_s13148_019_0697_y crossref_primary_10_1016_j_jtho_2023_07_012 crossref_primary_10_1084_jem_20231820 crossref_primary_10_4049_jimmunol_1800224 crossref_primary_10_1038_s41419_022_05124_w crossref_primary_10_1089_gtmb_2012_0449 crossref_primary_10_1093_bioinformatics_btaa291 crossref_primary_10_1038_s41563_020_0783_8 crossref_primary_10_1093_bioinformatics_btq511 crossref_primary_10_1016_j_micres_2021_126911 crossref_primary_10_1016_j_jbiotec_2016_11_020 crossref_primary_10_4137_BBI_S28991 crossref_primary_10_1016_j_bbagen_2016_07_017 crossref_primary_10_1093_bioinformatics_btx179 crossref_primary_10_1073_pnas_1402352111 crossref_primary_10_1016_j_celrep_2016_07_036 crossref_primary_10_1126_scitranslmed_aay1005 crossref_primary_10_1080_15548627_2024_2425594 crossref_primary_10_1002_btpr_2137 crossref_primary_10_3390_genes11121430 crossref_primary_10_1186_s12864_019_6127_x crossref_primary_10_1109_JTEHM_2024_3360865 crossref_primary_10_1007_s00421_018_3984_y crossref_primary_10_1002_pmic_201400392 crossref_primary_10_1016_j_ejphar_2015_03_021 crossref_primary_10_1016_j_stem_2020_08_001 crossref_primary_10_1038_s41598_022_19135_2 crossref_primary_10_3390_cimb46070459 crossref_primary_10_1101_gr_212241_116 crossref_primary_10_1038_s41598_017_09241_x crossref_primary_10_1038_ncomms5724 crossref_primary_10_1038_s41467_018_08003_1 crossref_primary_10_1152_ajprenal_00416_2023 crossref_primary_10_7554_eLife_91783 crossref_primary_10_1016_j_cmet_2013_10_001 crossref_primary_10_1093_hmg_ddae185 crossref_primary_10_1038_nm_3877 crossref_primary_10_1096_fj_201802281R crossref_primary_10_1016_j_bcp_2023_115925 crossref_primary_10_3390_cancers14194605 crossref_primary_10_1186_s12870_023_04255_2 crossref_primary_10_1371_journal_pone_0176950 crossref_primary_10_1038_s41467_020_17873_3 crossref_primary_10_3389_fimmu_2019_03103 crossref_primary_10_3390_cancers16040686 crossref_primary_10_1126_sciadv_abd7819 crossref_primary_10_1242_dev_196147 crossref_primary_10_1093_nar_gkx1314 crossref_primary_10_1128_iai_00103_24 crossref_primary_10_3390_cancers13205184 crossref_primary_10_1002_advs_202306268 crossref_primary_10_1096_fj_201801127R crossref_primary_10_1016_j_immuni_2024_05_023 crossref_primary_10_1038_srep15591 crossref_primary_10_1182_blood_2018_05_849893 crossref_primary_10_1126_scitranslmed_abp9675 crossref_primary_10_1371_journal_pone_0037510 crossref_primary_10_1016_j_jbi_2016_12_014 crossref_primary_10_1093_hmg_ddv315 crossref_primary_10_1186_s40168_025_02049_2 crossref_primary_10_1016_j_cell_2020_01_006 crossref_primary_10_1016_j_csbj_2021_04_056 crossref_primary_10_1016_j_crmeth_2022_100260 crossref_primary_10_1016_j_celrep_2017_08_081 crossref_primary_10_1186_1756_0500_5_370 crossref_primary_10_1016_j_celrep_2019_04_062 crossref_primary_10_1038_s41598_019_43006_y crossref_primary_10_1523_JNEUROSCI_1412_17_2017 crossref_primary_10_3390_nu14030611 crossref_primary_10_1038_s41467_018_07478_2 crossref_primary_10_1111_1751_7915_13738 crossref_primary_10_1007_s00425_017_2786_5 crossref_primary_10_1007_s10620_013_2873_9 crossref_primary_10_1016_j_celrep_2023_112237 crossref_primary_10_1111_jdv_16453 crossref_primary_10_3390_genes13091645 crossref_primary_10_1096_fj_202101135R crossref_primary_10_1371_journal_pone_0115585 crossref_primary_10_3390_microorganisms10010097 crossref_primary_10_1111_joim_12286 crossref_primary_10_1016_j_compbiolchem_2015_07_004 crossref_primary_10_1002_jor_25316 crossref_primary_10_1016_j_bbi_2018_03_016 crossref_primary_10_1093_nar_gkw731 crossref_primary_10_1093_ajcp_aqad003 crossref_primary_10_3389_fimmu_2024_1373497 crossref_primary_10_1016_j_yexcr_2020_111885 crossref_primary_10_1161_JAHA_120_017995 crossref_primary_10_4137_BBI_S30884 crossref_primary_10_1016_j_oraloncology_2019_104488 crossref_primary_10_3390_ijms24065115 crossref_primary_10_3389_fimmu_2022_958200 crossref_primary_10_1002_ijc_32384 crossref_primary_10_1038_s41467_021_25432_7 crossref_primary_10_1093_bioinformatics_btae119 crossref_primary_10_1038_s41590_023_01658_z crossref_primary_10_3390_nu8090543 crossref_primary_10_1186_s12859_018_2486_6 crossref_primary_10_1038_s41598_022_26843_2 crossref_primary_10_1093_stmcls_sxae079 crossref_primary_10_1126_sciimmunol_adl2986 crossref_primary_10_1371_journal_pone_0068288 crossref_primary_10_1038_s41598_023_33174_3 crossref_primary_10_3389_fmicb_2019_00967 crossref_primary_10_1155_2018_3028290 crossref_primary_10_1186_s13058_019_1111_6 crossref_primary_10_2119_molmed_2011_00286 crossref_primary_10_1177_1753425920966645 crossref_primary_10_1128_aem_01320_23 crossref_primary_10_1002_jnr_70025 crossref_primary_10_1242_dmm_033316 crossref_primary_10_1038_s41598_019_48493_7 crossref_primary_10_1098_rspb_2019_2019 crossref_primary_10_1016_j_celrep_2023_113157 crossref_primary_10_1074_jbc_M114_568535 crossref_primary_10_3390_ijms25084439 crossref_primary_10_1007_s10517_023_05686_5 crossref_primary_10_1186_s13059_016_1140_8 crossref_primary_10_1016_j_cmet_2022_07_012 crossref_primary_10_1016_j_esmoop_2021_100308 crossref_primary_10_1093_molehr_gay039 crossref_primary_10_1371_journal_pone_0140964 crossref_primary_10_1038_s41467_022_30299_3 crossref_primary_10_1080_08958378_2018_1533053 crossref_primary_10_1016_j_foodchem_2022_133769 crossref_primary_10_1038_s41598_023_34336_z crossref_primary_10_1007_s00277_025_06274_5 crossref_primary_10_1517_17530059_2012_718329 crossref_primary_10_3389_fonc_2022_978016 crossref_primary_10_1016_j_ajpath_2019_08_007 crossref_primary_10_1136_jitc_2023_007082 crossref_primary_10_1093_bioinformatics_btu864 crossref_primary_10_1534_g3_120_401270 crossref_primary_10_1038_s41598_018_31192_0 crossref_primary_10_3390_ijms23179653 crossref_primary_10_1007_s00203_024_04044_x crossref_primary_10_3389_fphys_2018_00797 crossref_primary_10_1186_s12979_015_0038_8 crossref_primary_10_7717_peerj_6994 crossref_primary_10_1074_jbc_RA119_010740 crossref_primary_10_1161_JAHA_120_019904 crossref_primary_10_1038_s41598_022_23544_8 crossref_primary_10_1016_j_gpb_2019_10_003 crossref_primary_10_1038_s41388_018_0554_z crossref_primary_10_1038_srep32523 crossref_primary_10_1186_s12864_016_2964_z crossref_primary_10_1371_journal_pone_0137797 crossref_primary_10_1002_jor_24025 crossref_primary_10_1080_14728222_2021_2013801 crossref_primary_10_1038_s41467_021_24981_1 crossref_primary_10_3389_fnmol_2018_00219 crossref_primary_10_1038_pr_2012_200 crossref_primary_10_1038_s41591_023_02785_8 crossref_primary_10_7554_eLife_64478 crossref_primary_10_3390_cells11233776 crossref_primary_10_3390_cancers14194653 crossref_primary_10_1186_s12915_020_00779_3 crossref_primary_10_18632_oncotarget_24517 crossref_primary_10_1016_j_envint_2015_05_012 crossref_primary_10_1126_scitranslmed_aaq0305 crossref_primary_10_1186_s12864_017_3711_9 crossref_primary_10_1126_sciadv_1500795 crossref_primary_10_1021_jacsau_2c00681 crossref_primary_10_1073_pnas_1920377117 crossref_primary_10_1109_TCBB_2021_3067613 crossref_primary_10_3389_fgene_2021_780113 crossref_primary_10_1016_j_scitotenv_2024_170865 crossref_primary_10_1038_s41398_018_0132_8 crossref_primary_10_1186_s12903_018_0520_8 crossref_primary_10_1371_journal_pone_0178281 crossref_primary_10_1038_s41598_018_24290_6 crossref_primary_10_1530_JME_13_0072 crossref_primary_10_1101_gr_124370_111 crossref_primary_10_1530_ERC_22_0108 crossref_primary_10_1016_j_chemosphere_2015_09_107 crossref_primary_10_1186_1748_7188_7_1 crossref_primary_10_1021_acsnano_3c02251 crossref_primary_10_1093_nar_gkw355 crossref_primary_10_1038_s43018_022_00486_8 crossref_primary_10_1186_s13059_018_1503_4 crossref_primary_10_3390_cancers13194801 crossref_primary_10_1016_j_fertnstert_2016_10_021 crossref_primary_10_7554_eLife_54659 crossref_primary_10_1152_ajpgi_00139_2013 crossref_primary_10_1371_journal_pone_0134011 crossref_primary_10_1128_IAI_00490_17 crossref_primary_10_1016_j_molmet_2018_08_005 crossref_primary_10_1016_j_fm_2018_09_012 crossref_primary_10_1111_1751_7915_13571 crossref_primary_10_1186_s12859_016_1125_3 crossref_primary_10_1101_gad_270959_115 crossref_primary_10_3168_jds_2020_19302 crossref_primary_10_1038_s41598_019_56017_6 crossref_primary_10_1093_nar_gkt054 crossref_primary_10_1038_s41598_019_54336_2 crossref_primary_10_3390_cancers13030412 crossref_primary_10_7554_eLife_67954 crossref_primary_10_1002_hep4_1701 crossref_primary_10_1016_j_cell_2024_07_030 crossref_primary_10_1016_j_jid_2024_02_038 crossref_primary_10_12688_f1000research_12544_1 crossref_primary_10_1186_s12863_020_00858_y crossref_primary_10_1093_bib_bbae498 crossref_primary_10_1093_bioinformatics_btw833 crossref_primary_10_3390_vaccines9111340 crossref_primary_10_1016_j_exger_2014_03_017 crossref_primary_10_1038_s41598_018_31659_0 crossref_primary_10_1038_s41467_018_06094_4 crossref_primary_10_1186_1752_0509_8_S4_S5 crossref_primary_10_1186_1471_2105_12_81 crossref_primary_10_1039_C9EN01144G crossref_primary_10_1016_j_xcrm_2021_100477 crossref_primary_10_1371_journal_pone_0150705 crossref_primary_10_3389_fcell_2025_1551090 crossref_primary_10_1007_s00401_020_02185_z crossref_primary_10_1096_fj_201801584R crossref_primary_10_1016_j_mbs_2014_09_005 crossref_primary_10_3390_ijms23115884 crossref_primary_10_1016_j_ccr_2011_07_013 crossref_primary_10_1093_bioinformatics_btw623 crossref_primary_10_1016_j_fertnstert_2017_08_018 crossref_primary_10_18632_oncotarget_24311 crossref_primary_10_1093_database_baw146 crossref_primary_10_3390_ijms24032872 crossref_primary_10_1016_j_ejca_2021_03_005 crossref_primary_10_3389_fvets_2024_1386135 crossref_primary_10_1038_s41564_024_01802_x crossref_primary_10_1002_1878_0261_12359 crossref_primary_10_1371_journal_pbio_1000472 crossref_primary_10_3390_cells12081102 crossref_primary_10_3390_cancers13102306 crossref_primary_10_1038_s41467_024_48471_2 crossref_primary_10_1128_AEM_03093_15 crossref_primary_10_1021_acs_molpharmaceut_3c00425 crossref_primary_10_1038_s44161_022_00139_0 crossref_primary_10_1002_hep4_1945 crossref_primary_10_1186_s12859_018_2139_9 crossref_primary_10_1038_s41598_018_25800_2 crossref_primary_10_1093_ecco_jcc_jjz014 crossref_primary_10_1038_s41467_024_52215_7 crossref_primary_10_1038_s42003_020_01353_x crossref_primary_10_1186_s12864_015_1661_7 crossref_primary_10_1038_s41598_018_27912_1 crossref_primary_10_1016_j_cmet_2020_01_006 crossref_primary_10_1371_journal_pgen_1007109 crossref_primary_10_3390_v14030506 crossref_primary_10_1002_cm_21761 crossref_primary_10_1093_bfgp_elq021 crossref_primary_10_1038_s41467_022_33850_4 crossref_primary_10_1084_jem_20192191 crossref_primary_10_1164_rccm_202103_0569OC crossref_primary_10_1242_dev_198374 crossref_primary_10_1016_j_bioflm_2021_100051 crossref_primary_10_1007_s00441_017_2734_5 crossref_primary_10_1038_srep30975 crossref_primary_10_1016_j_gene_2015_12_016 crossref_primary_10_1371_journal_pbio_3001753 crossref_primary_10_1038_nature24678 crossref_primary_10_3389_fcimb_2022_920425 crossref_primary_10_3389_fgene_2019_00904 crossref_primary_10_1007_s00520_017_3883_5 crossref_primary_10_1126_science_aan3975 crossref_primary_10_1371_journal_pone_0148818 crossref_primary_10_1038_s41390_020_0912_8 crossref_primary_10_1084_jem_20172323 crossref_primary_10_1016_j_molcel_2021_09_024 crossref_primary_10_1038_srep41623 crossref_primary_10_1038_s41467_019_13086_5 crossref_primary_10_1016_j_celrep_2024_114102 crossref_primary_10_1093_bioinformatics_btaa619 crossref_primary_10_1200_PO_16_00011 crossref_primary_10_1038_s41598_021_82606_5 crossref_primary_10_1186_s13059_024_03376_7 crossref_primary_10_1111_ina_12459 crossref_primary_10_1080_15592324_2017_1312242 crossref_primary_10_3389_fcimb_2017_00066 crossref_primary_10_1089_omi_2013_0017 crossref_primary_10_1182_blood_2022016889 crossref_primary_10_1371_journal_pcbi_1002053 crossref_primary_10_1186_s12864_017_4236_y crossref_primary_10_1016_j_celrep_2022_110636 crossref_primary_10_18632_oncotarget_6638 crossref_primary_10_1158_2767_9764_CRC_23_0091 crossref_primary_10_3390_cells8111410 crossref_primary_10_2337_db16_1305 crossref_primary_10_1016_j_chom_2019_09_001 crossref_primary_10_1186_s12859_015_0763_1 crossref_primary_10_1038_s41598_023_36567_6 crossref_primary_10_1038_onc_2013_298 crossref_primary_10_3390_genes12060901 crossref_primary_10_1016_j_chest_2021_12_668 crossref_primary_10_1111_bjd_20760 crossref_primary_10_15252_embj_2018100294 crossref_primary_10_1038_s41467_020_20207_y crossref_primary_10_1016_j_cub_2020_12_049 crossref_primary_10_1038_s41380_023_02403_6 crossref_primary_10_1016_j_jcmgh_2015_12_002 crossref_primary_10_7554_eLife_83140 crossref_primary_10_1016_j_xcrm_2020_100124 crossref_primary_10_1371_journal_pone_0247669 crossref_primary_10_1016_j_devcel_2022_11_012 crossref_primary_10_3390_e22040427 crossref_primary_10_1016_j_envpol_2018_10_123 crossref_primary_10_1371_journal_ppat_1009349 crossref_primary_10_3390_v10060332 crossref_primary_10_1038_s41467_020_19917_0 crossref_primary_10_1186_s12859_018_2308_x crossref_primary_10_1111_pce_14203 crossref_primary_10_1016_j_jconrel_2022_08_060 crossref_primary_10_1016_j_imu_2021_100563 crossref_primary_10_1002_advs_201801361 crossref_primary_10_1038_s44220_025_00387_6 crossref_primary_10_3390_genes12101523 crossref_primary_10_1371_journal_ppat_1012506 crossref_primary_10_1016_j_bonr_2021_101115 crossref_primary_10_15252_msb_202010141 crossref_primary_10_1089_cell_2015_0009 crossref_primary_10_1038_s41598_020_72955_y crossref_primary_10_1172_jci_insight_133232 crossref_primary_10_1016_j_jaci_2020_02_025 crossref_primary_10_18632_aging_203509 crossref_primary_10_1039_D1MO00106J crossref_primary_10_1186_s12915_018_0547_y crossref_primary_10_1038_s41388_020_1169_8 crossref_primary_10_1016_j_isci_2022_104625 crossref_primary_10_1093_gbe_evx014 crossref_primary_10_3389_fimmu_2018_02055 crossref_primary_10_3390_microorganisms8030421 crossref_primary_10_1017_pao_2019_3 crossref_primary_10_1093_nar_gkx372 crossref_primary_10_1038_s41564_022_01273_y crossref_primary_10_1186_s12859_023_05510_x crossref_primary_10_1016_j_modpat_2023_100251 crossref_primary_10_1007_s00262_020_02844_w crossref_primary_10_1093_bioinformatics_btt285 crossref_primary_10_1016_j_stem_2016_11_005 crossref_primary_10_1016_j_celrep_2022_111360 crossref_primary_10_1038_s41541_022_00471_3 crossref_primary_10_1038_srep34589 crossref_primary_10_3390_biom11050677 crossref_primary_10_4137_BBI_S33124 crossref_primary_10_1186_s13058_022_01562_8 crossref_primary_10_1101_gr_212720_116 crossref_primary_10_1186_s13073_022_01111_0 crossref_primary_10_3389_fvets_2020_558135 crossref_primary_10_1007_s10616_025_00724_8 crossref_primary_10_1038_s41467_021_22077_4 crossref_primary_10_3390_ijms21165923 crossref_primary_10_1038_s41598_019_51056_5 crossref_primary_10_1002_humu_23769 crossref_primary_10_1016_j_jbc_2022_101895 crossref_primary_10_1038_nm_4484 crossref_primary_10_1016_j_mce_2016_09_019 crossref_primary_10_1089_omi_2012_0084 crossref_primary_10_7554_eLife_40856 crossref_primary_10_1186_s13073_022_01037_7 crossref_primary_10_1007_s00441_022_03597_x crossref_primary_10_1038_s41598_022_22688_x crossref_primary_10_1128_jvi_01179_24 crossref_primary_10_18632_oncotarget_16417 crossref_primary_10_1038_s41598_021_88698_3 crossref_primary_10_1101_lm_051177_119 crossref_primary_10_1016_j_taap_2013_01_022 crossref_primary_10_1093_g3journal_jkab329 crossref_primary_10_1186_s12864_017_4168_6 crossref_primary_10_1038_s41598_022_21927_5 crossref_primary_10_1016_j_jbi_2013_09_004 crossref_primary_10_1158_1078_0432_CCR_19_1800 crossref_primary_10_1073_pnas_1600204113 crossref_primary_10_1242_jeb_210302 crossref_primary_10_1096_fj_201902731RR crossref_primary_10_1038_s41586_019_1263_7 crossref_primary_10_1080_20002297_2023_2246279 crossref_primary_10_3389_fmicb_2018_00752 crossref_primary_10_1038_ncb3389 crossref_primary_10_1158_0008_5472_BCD_19_0068 crossref_primary_10_7554_eLife_51503 crossref_primary_10_3389_fmars_2018_00429 crossref_primary_10_1038_s41467_018_06515_4 crossref_primary_10_3390_cancers13215519 crossref_primary_10_1038_s41598_017_13306_2 crossref_primary_10_1371_journal_pgen_1003494 crossref_primary_10_1002_1878_0261_12616 crossref_primary_10_1016_j_molcel_2023_06_011 crossref_primary_10_1093_bib_bbac435 crossref_primary_10_1186_s12885_018_5061_7 crossref_primary_10_15252_emmm_202216805 crossref_primary_10_1128_spectrum_02528_22 crossref_primary_10_1093_bioinformatics_btv265 crossref_primary_10_1182_blood_2016_02_698027 crossref_primary_10_3390_ijms20235895 crossref_primary_10_7554_eLife_41930 crossref_primary_10_1038_s41598_020_62578_8 crossref_primary_10_1016_j_yjmcc_2023_04_002 crossref_primary_10_3389_fmolb_2023_1232573 crossref_primary_10_1016_j_bone_2024_117313 crossref_primary_10_1186_1471_2164_13_282 crossref_primary_10_1016_j_xcrm_2021_100434 crossref_primary_10_1172_JCI71386 crossref_primary_10_1093_molbev_msz171 crossref_primary_10_1136_gutjnl_2015_309957 crossref_primary_10_1371_journal_pone_0145801 crossref_primary_10_1038_cr_2011_149 crossref_primary_10_1016_j_isci_2022_103956 crossref_primary_10_1038_s41467_024_46983_5 crossref_primary_10_1038_s41598_024_73632_0 crossref_primary_10_1111_tpj_14988 crossref_primary_10_15252_msb_20209596 crossref_primary_10_3389_fgene_2018_00108 crossref_primary_10_1016_j_celrep_2024_115096 crossref_primary_10_1016_j_micres_2024_127997 crossref_primary_10_1158_2326_6066_CIR_18_0070 crossref_primary_10_18632_oncotarget_15125 crossref_primary_10_1016_j_bbi_2018_04_004 crossref_primary_10_1016_j_isci_2024_108960 crossref_primary_10_1038_s41467_023_43623_2 crossref_primary_10_1128_AEM_01462_19 crossref_primary_10_3389_fimmu_2023_1198665 crossref_primary_10_1016_j_chest_2019_03_040 crossref_primary_10_1083_jcb_202305048 crossref_primary_10_1186_1752_0509_5_82 crossref_primary_10_1371_journal_pone_0154531 crossref_primary_10_1080_15592294_2015_1039216 crossref_primary_10_1128_mSystems_00495_20 crossref_primary_10_1371_journal_pbio_2006506 crossref_primary_10_1186_s12864_016_3367_x crossref_primary_10_3389_fgene_2016_00044 crossref_primary_10_1128_IAI_00587_19 crossref_primary_10_18632_aging_205557 crossref_primary_10_1016_j_celrep_2021_109803 crossref_primary_10_1016_j_scr_2015_11_001 crossref_primary_10_1126_science_aau4732 crossref_primary_10_1002_ana_24944 crossref_primary_10_1039_D1EN00735A crossref_primary_10_1038_s41598_020_70173_0 crossref_primary_10_1016_j_molonc_2014_02_002 crossref_primary_10_1186_s13568_014_0071_6 crossref_primary_10_3390_ijms241411669 crossref_primary_10_1016_j_cbd_2015_07_005 crossref_primary_10_3389_fimmu_2022_1007022 crossref_primary_10_1186_s12864_019_5974_9 crossref_primary_10_1038_ng_2916 crossref_primary_10_1038_s41467_020_14777_0 crossref_primary_10_1038_s41598_020_72511_8 crossref_primary_10_3390_cancers13184562 crossref_primary_10_3390_metabo14030159 crossref_primary_10_1155_2018_7342472 crossref_primary_10_1164_rccm_201503_0558OC crossref_primary_10_3389_fped_2022_908524 crossref_primary_10_1186_s12859_017_1571_6 crossref_primary_10_3390_app11062678 crossref_primary_10_1182_bloodadvances_2022007652 crossref_primary_10_1038_s41467_020_20138_8 crossref_primary_10_1186_s12864_015_1810_z crossref_primary_10_1038_s41467_021_22981_9 crossref_primary_10_7554_eLife_45009 crossref_primary_10_1016_j_devcel_2022_10_002 crossref_primary_10_3390_ijms24119355 crossref_primary_10_18632_oncotarget_20957 crossref_primary_10_1371_journal_pone_0079217 crossref_primary_10_1093_jmcb_mjae030 crossref_primary_10_1371_journal_pone_0115597 crossref_primary_10_1038_s41589_019_0421_4 crossref_primary_10_1038_s43705_022_00167_8 crossref_primary_10_3390_cells11030464 crossref_primary_10_1186_s12864_020_6589_x crossref_primary_10_1038_s43587_022_00293_x crossref_primary_10_1002_pmic_201400296 crossref_primary_10_1158_1078_0432_CCR_18_3143 crossref_primary_10_1371_journal_pbio_3000091 crossref_primary_10_1007_s00497_022_00452_5 crossref_primary_10_1016_j_ymeth_2017_05_026 crossref_primary_10_1093_braincomms_fcae147 crossref_primary_10_1016_j_aquaculture_2024_741385 crossref_primary_10_1186_s12864_020_6467_6 crossref_primary_10_1021_acs_jafc_8b06361 crossref_primary_10_1016_j_jaci_2022_03_025 crossref_primary_10_1016_j_micpath_2023_105970 crossref_primary_10_1159_000511893 crossref_primary_10_1073_pnas_1620993114 crossref_primary_10_1128_JVI_00924_15 crossref_primary_10_1186_s12967_022_03855_0 crossref_primary_10_1016_j_pnpbp_2020_110086 crossref_primary_10_1371_journal_pbio_3001166 crossref_primary_10_1016_j_celrep_2020_108416 crossref_primary_10_3389_fbinf_2024_1380928 crossref_primary_10_1126_sciimmunol_aan3796 crossref_primary_10_1084_jem_20181505 crossref_primary_10_1172_jci_insight_140332 crossref_primary_10_1038_s41698_020_0125_y crossref_primary_10_1126_scitranslmed_aal4069 crossref_primary_10_1016_j_cris_2024_100104 crossref_primary_10_3389_fnins_2023_1148683 crossref_primary_10_1016_j_jvs_2010_02_282 crossref_primary_10_1101_gr_202200_115 crossref_primary_10_1242_dmm_049721 crossref_primary_10_1038_s42003_021_02703_z crossref_primary_10_3390_nu14235007 crossref_primary_10_1016_j_celrep_2021_108941 crossref_primary_10_1126_sciimmunol_adj5948 crossref_primary_10_1038_nature20789 crossref_primary_10_1111_liv_13362 crossref_primary_10_1158_1078_0432_CCR_17_0246 crossref_primary_10_1016_j_jaci_2017_10_046 crossref_primary_10_2337_db20_0763 crossref_primary_10_1002_1873_3468_13621 crossref_primary_10_1172_jci_insight_128014 crossref_primary_10_1038_s41598_024_61807_8 crossref_primary_10_1038_srep36966 crossref_primary_10_1038_s41420_018_0027_8 crossref_primary_10_1038_s41563_022_01293_3 crossref_primary_10_3389_fpls_2022_893652 crossref_primary_10_3390_ijms23179821 crossref_primary_10_1186_s12918_018_0674_7 crossref_primary_10_3390_genes14050997 crossref_primary_10_3389_fonc_2021_705627 crossref_primary_10_1038_s41591_018_0124_5 crossref_primary_10_3389_fpls_2020_01269 crossref_primary_10_1038_s41379_022_01143_2 crossref_primary_10_1038_s41598_021_99760_5 crossref_primary_10_1038_s41523_021_00277_x crossref_primary_10_1038_ncomms15055 crossref_primary_10_1016_j_jid_2018_05_017 crossref_primary_10_1016_j_ymgme_2016_08_001 crossref_primary_10_1177_19458924221134732 crossref_primary_10_1016_j_bbi_2015_12_010 crossref_primary_10_2139_ssrn_3205407 crossref_primary_10_1016_j_foodres_2023_113466 crossref_primary_10_1007_s00344_015_9538_1 crossref_primary_10_1016_j_heliyon_2024_e32949 crossref_primary_10_1002_jbm4_10071 crossref_primary_10_1038_s41598_023_34390_7 crossref_primary_10_3389_fimmu_2022_809414 crossref_primary_10_7554_eLife_59831 crossref_primary_10_1371_journal_pone_0101271 crossref_primary_10_1038_s41396_018_0312_9 crossref_primary_10_1111_1759_7714_14089 crossref_primary_10_3390_cancers16213640 crossref_primary_10_1016_j_canlet_2018_03_048 crossref_primary_10_1016_j_ebiom_2018_10_037 crossref_primary_10_1371_journal_pgen_1007272 crossref_primary_10_1007_s00125_020_05296_0 crossref_primary_10_1186_s13287_020_02111_w crossref_primary_10_1165_rcmb_2015_0274MA crossref_primary_10_1097_HC9_0000000000000478 crossref_primary_10_1152_physiolgenomics_00103_2016 crossref_primary_10_1016_j_jcmgh_2023_09_007 crossref_primary_10_1111_ics_13017 crossref_primary_10_1128_iai_00035_23 crossref_primary_10_3389_fgene_2020_586658 crossref_primary_10_1242_jeb_217406 crossref_primary_10_1002_ajb2_1384 crossref_primary_10_1093_bib_bbv044 crossref_primary_10_1111_jnc_15313 crossref_primary_10_1093_bjsopen_zraa045 crossref_primary_10_1186_1471_2105_14_S5_S16 crossref_primary_10_1016_j_neuroscience_2020_06_009 crossref_primary_10_1186_s40659_016_0095_2 crossref_primary_10_1038_s41559_018_0689_x crossref_primary_10_3389_fmicb_2022_1069443 crossref_primary_10_3389_fmicb_2022_904451 crossref_primary_10_1093_biolinnean_blac151 crossref_primary_10_3389_fonc_2020_01383 crossref_primary_10_1371_journal_pone_0147027 crossref_primary_10_1016_j_cmet_2017_03_017 crossref_primary_10_1142_S0192415X17500756 crossref_primary_10_3389_fgene_2018_00396 crossref_primary_10_1093_biolre_ioaf027 crossref_primary_10_3389_fpls_2021_745855 crossref_primary_10_1016_j_cmet_2020_11_008 crossref_primary_10_4137_CIN_S13305 crossref_primary_10_1016_j_isci_2024_108934 crossref_primary_10_1039_D1FO04420F crossref_primary_10_1016_j_lungcan_2025_108448 crossref_primary_10_1002_jcsm_12294 crossref_primary_10_3390_cancers11081049 crossref_primary_10_3389_fmicb_2024_1385775 crossref_primary_10_1016_j_ygeno_2011_09_001 crossref_primary_10_1038_ki_2012_487 crossref_primary_10_1016_j_kint_2024_08_021 crossref_primary_10_1371_journal_pone_0190817 crossref_primary_10_1016_j_celrep_2018_02_027 crossref_primary_10_3389_fimmu_2023_1215607 crossref_primary_10_3390_nu12051281 crossref_primary_10_1016_j_cbd_2015_04_001 crossref_primary_10_1016_j_cmet_2017_09_006 crossref_primary_10_1016_j_prp_2024_155604 crossref_primary_10_1128_IAI_00277_16 crossref_primary_10_1016_j_celrep_2021_108751 crossref_primary_10_1038_s41467_020_20598_y crossref_primary_10_1093_infdis_jiw461 crossref_primary_10_1126_sciadv_aar8590 crossref_primary_10_1002_mds_28506 crossref_primary_10_1016_j_cbd_2021_100906 crossref_primary_10_1002_ar_22992 crossref_primary_10_1016_j_chemosphere_2017_12_029 crossref_primary_10_1038_s41598_022_13396_7 crossref_primary_10_1128_msphere_00273_23 crossref_primary_10_1002_glia_24383 crossref_primary_10_1016_j_celrep_2020_108228 crossref_primary_10_1038_s41598_025_85187_9 crossref_primary_10_1158_1541_7786_MCR_19_1170 crossref_primary_10_1158_2159_8290_CD_12_0116 crossref_primary_10_1210_en_2018_00567 crossref_primary_10_1038_s42255_022_00552_6 crossref_primary_10_1038_s42255_020_0261_2 crossref_primary_10_3389_fimmu_2023_1058267 crossref_primary_10_1002_pld3_159 crossref_primary_10_1038_s41416_023_02196_z crossref_primary_10_1016_j_cmet_2019_08_020 crossref_primary_10_18632_aging_202179 crossref_primary_10_1016_j_prp_2023_154674 crossref_primary_10_3390_cancers15082336 crossref_primary_10_4236_ajps_2018_96098 crossref_primary_10_1038_s41598_018_30735_9 crossref_primary_10_3389_fneur_2023_1267136 crossref_primary_10_1038_mp_2015_167 crossref_primary_10_1038_s41467_023_38020_8 crossref_primary_10_1111_mec_16610 crossref_primary_10_3389_fcimb_2018_00326 crossref_primary_10_1016_j_foodres_2019_02_004 crossref_primary_10_1038_s41598_018_32682_x crossref_primary_10_1093_jnci_djy234 crossref_primary_10_1016_j_cbd_2018_10_007 crossref_primary_10_3390_ijms26030979 crossref_primary_10_1186_1471_2105_15_146 crossref_primary_10_1186_1745_6150_7_44 crossref_primary_10_1371_journal_pone_0272117 crossref_primary_10_1016_j_celrep_2018_07_058 crossref_primary_10_1111_exd_14481 crossref_primary_10_1002_stem_3241 crossref_primary_10_1371_journal_pone_0275864 crossref_primary_10_1155_2016_7972351 crossref_primary_10_1186_s12885_020_07058_y crossref_primary_10_3390_foods10020314 crossref_primary_10_1186_s12859_015_0722_x crossref_primary_10_1016_j_cels_2016_01_014 crossref_primary_10_1007_s10528_023_10453_2 crossref_primary_10_1111_jdv_17846 crossref_primary_10_1186_s40246_019_0226_2 crossref_primary_10_1109_TCBB_2015_2453948 crossref_primary_10_18632_oncotarget_11309 crossref_primary_10_18632_oncotarget_20260 crossref_primary_10_1161_ATVBAHA_115_306415 crossref_primary_10_1016_j_foodres_2023_113851 crossref_primary_10_1038_s41417_022_00444_7 crossref_primary_10_1007_s00253_024_13379_w crossref_primary_10_1093_aje_kwz193 crossref_primary_10_1371_journal_pgen_1006336 crossref_primary_10_1371_journal_pgen_1009603 crossref_primary_10_1038_s41413_022_00236_7 crossref_primary_10_1186_s12864_021_07502_8 crossref_primary_10_3389_fimmu_2020_584310 crossref_primary_10_1038_s41467_018_03836_2 crossref_primary_10_1038_s43587_024_00775_0 crossref_primary_10_1086_719397 crossref_primary_10_1093_hmg_ddab012 crossref_primary_10_1016_j_foodres_2021_110379 crossref_primary_10_3390_ijerph17155466 crossref_primary_10_3390_microorganisms9040717 crossref_primary_10_1038_srep06347 crossref_primary_10_1186_s12864_016_2493_9 crossref_primary_10_1111_mec_14088 crossref_primary_10_1186_s12864_021_08051_w crossref_primary_10_1186_s12974_017_0967_6 crossref_primary_10_7554_eLife_91783_5 crossref_primary_10_1016_j_xcrm_2021_100349 crossref_primary_10_1038_s41467_024_48704_4 crossref_primary_10_1126_science_adk9167 crossref_primary_10_1016_j_compbiomed_2016_09_010 crossref_primary_10_1016_j_gpb_2020_06_014 crossref_primary_10_1016_j_trsl_2022_03_009 crossref_primary_10_1016_j_jgg_2018_08_002 crossref_primary_10_1089_regen_2022_0032 crossref_primary_10_1016_j_tim_2013_04_009 crossref_primary_10_1016_j_kint_2023_11_026 crossref_primary_10_1093_molbev_msac219 crossref_primary_10_1002_mnfr_201800424 crossref_primary_10_1016_j_cels_2021_08_002 crossref_primary_10_1016_j_copbio_2012_10_017 crossref_primary_10_1186_s13059_016_1126_6 crossref_primary_10_1186_s12931_025_03094_z crossref_primary_10_1371_journal_pcbi_1004393 crossref_primary_10_1038_s41413_020_00109_x crossref_primary_10_1186_s12964_021_00801_3 crossref_primary_10_1016_j_cell_2023_05_038 crossref_primary_10_3389_fbinf_2024_1280971 crossref_primary_10_1038_s41467_022_34487_z crossref_primary_10_1016_j_immuni_2023_07_002 crossref_primary_10_1128_mSystems_00061_19 crossref_primary_10_3389_fvets_2022_848027 crossref_primary_10_1094_PHYTO_04_24_0138_R crossref_primary_10_1016_j_jaci_2020_04_059 crossref_primary_10_1093_bioinformatics_bts389 crossref_primary_10_1186_1471_2407_12_232 crossref_primary_10_1016_j_cpnec_2023_100201 crossref_primary_10_1093_hmg_ddy051 crossref_primary_10_3390_metabo12060484 crossref_primary_10_3390_biology9120435 crossref_primary_10_1093_nsr_nwaa124 crossref_primary_10_1002_agt2_666 crossref_primary_10_1038_s41598_018_23537_6 crossref_primary_10_1371_journal_pone_0192606 crossref_primary_10_1002_1878_0261_12012 crossref_primary_10_1136_gutjnl_2016_311447 crossref_primary_10_1073_pnas_2311422120 crossref_primary_10_1101_gad_322776_118 crossref_primary_10_1111_ene_13565 crossref_primary_10_1016_j_prp_2022_154198 crossref_primary_10_3390_ijms21186946 crossref_primary_10_1126_scitranslmed_abb8969 crossref_primary_10_1038_s41467_024_51859_9 crossref_primary_10_1002_bit_26290 crossref_primary_10_1128_Spectrum_01751_21 crossref_primary_10_1016_j_celrep_2022_110733 crossref_primary_10_1089_omi_2018_0060 crossref_primary_10_7554_eLife_53403 crossref_primary_10_1002_adtp_202400374 crossref_primary_10_1098_rspb_2020_0956 crossref_primary_10_1128_msystems_00193_20 crossref_primary_10_1101_mcs_a002329 crossref_primary_10_1038_s41598_020_59541_y crossref_primary_10_1186_s12920_019_0498_3 crossref_primary_10_1134_S0026893318040076 crossref_primary_10_1073_pnas_2105428118 crossref_primary_10_1186_s12864_015_2208_7 crossref_primary_10_1016_j_scienta_2021_110321 crossref_primary_10_1136_jitc_2020_000544 crossref_primary_10_1186_1471_2164_14_589 crossref_primary_10_15252_embj_2018101323 crossref_primary_10_1038_s41467_022_33324_7 crossref_primary_10_1038_s41598_024_73023_5 crossref_primary_10_1038_s41586_023_05769_3 crossref_primary_10_3390_v13061140 crossref_primary_10_1128_msystems_00451_19 crossref_primary_10_1093_sleep_zsaa188 crossref_primary_10_3390_cancers15133330 crossref_primary_10_1016_j_compbiolchem_2020_107285 crossref_primary_10_1134_S1022795419030104 crossref_primary_10_3389_fcimb_2022_938286 crossref_primary_10_1038_s41467_023_41629_4 crossref_primary_10_1016_j_nbd_2022_105983 crossref_primary_10_1016_j_chembiol_2017_03_016 crossref_primary_10_1111_cmi_12993 crossref_primary_10_1126_sciadv_adj5428 crossref_primary_10_1016_j_bone_2019_03_020 crossref_primary_10_14814_phy2_13019 crossref_primary_10_1002_prp2_243 |
| Cites_doi | 10.1016/j.lungcan.2006.03.015 10.1073/pnas.0506577102 10.1038/nm733 10.1186/1471-2105-9-303 10.1093/bib/bbn001 10.1002/path.1785 10.1002/cncr.22922 10.1074/jbc.M301053200 10.1186/1476-4598-1-5 10.1359/jbmr.2003.18.10.1842 10.1073/pnas.0506580102 10.1214/07-AOAS104 10.1126/science.8197455 10.2307/2683975 10.1038/nature05354 10.1093/nar/gki475 10.1186/1471-2105-8-S6-S6 10.1359/JBMR.0301249 10.1093/bioinformatics/btl599 10.1038/ng1180 10.1186/1471-2105-6-144 10.1093/bioinformatics/bti260 10.1186/1476-4598-6-71 10.1186/1471-2164-8-70 10.1093/bioinformatics/btg405 10.1093/nar/gkl766 10.1186/1471-2105-9-467 10.1146/annurev.ps.46.020195.003021 10.1083/jcb.200211021 10.1158/0008-5472.CAN-06-4571 10.1016/j.clon.2007.04.002 10.1093/bioinformatics/bth293 10.1186/1471-2105-8-114 10.1038/sj.onc.1208920 10.1080/02664769823304 10.1093/nar/gki484 10.1093/bioinformatics/btm116 10.1186/1471-2105-8-431 10.1002/jcb.10515 10.1073/pnas.191502998 10.1186/1471-2105-8-242 10.1093/bioinformatics/btm051 10.1038/nrc2069 10.1016/S0014-4827(02)00013-7 10.1093/nar/gni179 |
| ContentType | Journal Article |
| Copyright | Luo et al; licensee BioMed Central Ltd. 2009 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. COPYRIGHT 2009 BioMed Central Ltd. Copyright © 2009 Luo et al; licensee BioMed Central Ltd. 2009 Luo et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Luo et al; licensee BioMed Central Ltd. 2009 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: COPYRIGHT 2009 BioMed Central Ltd. – notice: Copyright © 2009 Luo et al; licensee BioMed Central Ltd. 2009 Luo et al; licensee BioMed Central Ltd. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/1471-2105-10-161 |
| DatabaseName | Springer Nature OA Free Journals (WRLC) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: SpringerOpen url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 161 |
| ExternalDocumentID | oai_doaj_org_article_70693835e8c24f0299e49797e7b34ac2 10.1186/1471-2105-10-161 PMC2696452 A201815046 19473525 10_1186_1471_2105_10_161 |
| Genre | Journal Article Research Support, N.I.H., Extramural |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GrantInformation_xml | – fundername: NIAMS NIH HHS grantid: R01 AR054714 – fundername: NIDA NIH HHS grantid: U54-DA-021519 – fundername: NIDCR NIH HHS grantid: R01 DE017471 – fundername: NIDA NIH HHS grantid: U54 DA021519 – fundername: NIAMS NIH HHS grantid: R01 AR049682 |
| GroupedDBID | --- 0R~ 23N 2VQ 2WC 4.4 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C1A C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAO ICD IHR INH INR IPNFZ ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RIG RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF M0N NPM 7X8 5PM 123 ADTOC AFFHD UNPAY |
| ID | FETCH-LOGICAL-c669t-55156d36b9d5b5a9c60c8a1a5b429f63de565851f78b4f3dfbc3ba97610c62db3 |
| IEDL.DBID | UNPAY |
| ISSN | 1471-2105 |
| IngestDate | Fri Oct 03 12:45:35 EDT 2025 Wed Oct 29 11:23:29 EDT 2025 Tue Sep 30 15:40:48 EDT 2025 Thu Oct 02 10:19:53 EDT 2025 Mon Oct 20 22:52:57 EDT 2025 Mon Oct 20 17:04:47 EDT 2025 Thu Oct 16 16:09:10 EDT 2025 Wed Feb 19 01:50:00 EST 2025 Wed Oct 01 04:15:15 EDT 2025 Thu Apr 24 23:02:15 EDT 2025 Sat Sep 06 07:21:13 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Large Clinical Dataset Diabetes Dataset Lung Cancer Dataset Gene Randomization Method Canonical Pathway |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c669t-55156d36b9d5b5a9c60c8a1a5b429f63de565851f78b4f3dfbc3ba97610c62db3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-10-161 |
| PMID | 19473525 |
| PQID | 67377927 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_70693835e8c24f0299e49797e7b34ac2 unpaywall_primary_10_1186_1471_2105_10_161 pubmedcentral_primary_oai_pubmedcentral_nih_gov_2696452 proquest_miscellaneous_67377927 gale_infotracmisc_A201815046 gale_infotracacademiconefile_A201815046 gale_incontextgauss_ISR_A201815046 pubmed_primary_19473525 crossref_primary_10_1186_1471_2105_10_161 crossref_citationtrail_10_1186_1471_2105_10_161 springer_journals_10_1186_1471_2105_10_161 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2009-05-27 |
| PublicationDateYYYYMMDD | 2009-05-27 |
| PublicationDate_xml | – month: 05 year: 2009 text: 2009-05-27 day: 27 |
| PublicationDecade | 2000 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2009 |
| Publisher | BioMed Central BioMed Central Ltd BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: BMC |
| References | V Saxena (2891_CR19) 2006; 34 D Nam (2891_CR2) 2008; 9 AS Kristof (2891_CR31) 2003; 278 Z Jiang (2891_CR41) 2007; 23 JA Baur (2891_CR17) 2006; 444 PR Montgrain (2891_CR28) 2007; 110 G Rawadi (2891_CR34) 2003; 18 W Luo (2891_CR1) 2008; 9 VK Mootha (2891_CR4) 2003; 34 A Bhattacharjee (2891_CR22) 2001; 98 I Dinu (2891_CR9) 2007; 8 B Kulterer (2891_CR35) 2007; 8 K Strimmer (2891_CR46) 2008; 9 DM Kemp (2891_CR20) 2007; 23 JM Stonehouse (2891_CR44) 1998; 25 2891_CR47 2891_CR48 2891_CR49 Q Liu (2891_CR12) 2007; 8 A Subramanian (2891_CR3) 2005; 102 SY Kim (2891_CR5) 2005; 6 M Dai (2891_CR53) 2005; 33 WJ Conover (2891_CR45) 1981; 35 L Gautier (2891_CR52) 2004; 20 D Nam (2891_CR15) 2008 J Larsson (2891_CR37) 2005; 24 RT Dorsam (2891_CR24) 2007; 7 D Antoniou (2891_CR27) 2006; 53 GE Hidalgo (2891_CR29) 2002; 1 DB Ong (2891_CR40) 2004; 19 2891_CR51 V Maguer-Satta (2891_CR38) 2003; 282 JE Darnell Jr (2891_CR30) 1994; 264 S Li (2891_CR25) 2005; 27 L Tian (2891_CR8) 2005; 102 JP Shaffer (2891_CR43) 1995; 46 B Zhang (2891_CR13) 2005; 33 MW Helms (2891_CR39) 2005; 206 F Al-Shahrour (2891_CR14) 2007; 8 G Altiay (2891_CR26) 2007; 19 MA Newton (2891_CR6) 2007; 1 E Balint (2891_CR36) 2003; 89 2891_CR23 JJ Goeman (2891_CR11) 2007; 23 WT Barry (2891_CR10) 2005; 21 HJ Bussemaker (2891_CR16) 2007; 8 K Fujita (2891_CR33) 2007; 6 Y Benjamini (2891_CR42) 1995; 57 P Rajan (2891_CR32) 2003; 161 M Smid (2891_CR18) 2004; 20 DG Beer (2891_CR21) 2002; 8 KA Furge (2891_CR50) 2007; 67 A Boorsma (2891_CR7) 2005; 33 15941488 - BMC Bioinformatics. 2005;6:144 12807916 - J Biol Chem. 2003 Sep 5;278(36):33637-44 12531697 - Exp Cell Res. 2003 Jan 15;282(2):110-20 16123801 - Oncogene. 2005 Aug 29;24(37):5676-92 17971207 - Mol Cancer. 2007;6:71 11707567 - Proc Natl Acad Sci U S A. 2001 Nov 20;98(24):13790-5 18202032 - Brief Bioinform. 2008 May;9(3):189-97 17407596 - BMC Bioinformatics. 2007;8:114 17903287 - BMC Bioinformatics. 2007;8 Suppl 6:S6 12459041 - Mol Cancer. 2002 Nov 12;1:5 15980575 - Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W741-8 18613966 - BMC Bioinformatics. 2008;9:303 15040833 - J Bone Miner Res. 2004 Mar;19(3):447-54 17127676 - Bioinformatics. 2007 Feb 1;23(3):306-13 17303618 - Bioinformatics. 2007 Apr 15;23(8):980-7 12704803 - J Cell Biochem. 2003 May 15;89(2):401-26 15980543 - Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W592-5 15647293 - Bioinformatics. 2005 May 1;21(9):1943-9 17392327 - Bioinformatics. 2007 Jun 1;23(11):1356-62 16174746 - Proc Natl Acad Sci U S A. 2005 Sep 20;102(38):13544-9 12118244 - Nat Med. 2002 Aug;8(8):816-24 16769149 - Lung Cancer. 2006 Aug;53(2):205-10 18980677 - BMC Bioinformatics. 2008;9:467 15130934 - Bioinformatics. 2004 Nov 1;20(16):2618-25 17612399 - BMC Bioinformatics. 2007;8:242 16284200 - Nucleic Acids Res. 2005;33(20):e175 17352823 - BMC Genomics. 2007;8:70 14584895 - J Bone Miner Res. 2003 Oct;18(10):1842-53 17130162 - Nucleic Acids Res. 2006;34(22):e151 17988400 - BMC Bioinformatics. 2007;8:431 17251915 - Nat Rev Cancer. 2007 Feb;7(2):79-94 17676588 - Cancer. 2007 Sep 15;110(6):1313-20 8197455 - Science. 1994 Jun 3;264(5164):1415-21 17513096 - Clin Oncol (R Coll Radiol). 2007 Sep;19(7):494-8 17409424 - Cancer Res. 2007 Apr 1;67(7):3171-6 12796477 - J Cell Biol. 2003 Jun 9;161(5):911-21 15892165 - J Pathol. 2005 Jul;206(3):366-76 12808457 - Nat Genet. 2003 Jul;34(3):267-73 16211229 - Int J Oncol. 2005 Nov;27(5):1329-39 17086191 - Nature. 2006 Nov 16;444(7117):337-42 14960456 - Bioinformatics. 2004 Feb 12;20(3):307-15 16199517 - Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 |
| References_xml | – volume: 53 start-page: 205 year: 2006 ident: 2891_CR27 publication-title: Lung Cancer doi: 10.1016/j.lungcan.2006.03.015 – volume: 102 start-page: 13544 year: 2005 ident: 2891_CR8 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.0506577102 – ident: 2891_CR49 – volume: 8 start-page: 816 year: 2002 ident: 2891_CR21 publication-title: Nat Med doi: 10.1038/nm733 – volume: 9 start-page: 303 year: 2008 ident: 2891_CR46 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-303 – volume: 9 start-page: 189 year: 2008 ident: 2891_CR2 publication-title: Brief Bioinform doi: 10.1093/bib/bbn001 – volume: 206 start-page: 366 year: 2005 ident: 2891_CR39 publication-title: J Pathol doi: 10.1002/path.1785 – volume-title: Brief Bioinform year: 2008 ident: 2891_CR15 – volume: 110 start-page: 1313 year: 2007 ident: 2891_CR28 publication-title: Cancer doi: 10.1002/cncr.22922 – volume: 278 start-page: 33637 year: 2003 ident: 2891_CR31 publication-title: J Biol Chem doi: 10.1074/jbc.M301053200 – volume: 1 start-page: 5 year: 2002 ident: 2891_CR29 publication-title: Mol Cancer doi: 10.1186/1476-4598-1-5 – volume: 18 start-page: 1842 year: 2003 ident: 2891_CR34 publication-title: J Bone Miner Res doi: 10.1359/jbmr.2003.18.10.1842 – ident: 2891_CR51 – volume: 102 start-page: 15545 year: 2005 ident: 2891_CR3 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.0506580102 – volume: 1 start-page: 85 year: 2007 ident: 2891_CR6 publication-title: Ann Appl Stat doi: 10.1214/07-AOAS104 – volume: 264 start-page: 1415 year: 1994 ident: 2891_CR30 publication-title: Science doi: 10.1126/science.8197455 – volume: 35 start-page: 124 year: 1981 ident: 2891_CR45 publication-title: American Statistician doi: 10.2307/2683975 – volume: 444 start-page: 337 year: 2006 ident: 2891_CR17 publication-title: Nature doi: 10.1038/nature05354 – volume: 33 start-page: W741 year: 2005 ident: 2891_CR13 publication-title: Nucleic Acids Res doi: 10.1093/nar/gki475 – volume: 8 start-page: S6 issue: Suppl 6 year: 2007 ident: 2891_CR16 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-S6-S6 – volume: 57 start-page: 289 year: 1995 ident: 2891_CR42 publication-title: Journal of the Royal Statistical Society Series B-Methodological – volume: 19 start-page: 447 year: 2004 ident: 2891_CR40 publication-title: J Bone Miner Res doi: 10.1359/JBMR.0301249 – volume: 23 start-page: 306 year: 2007 ident: 2891_CR41 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl599 – volume: 34 start-page: 267 year: 2003 ident: 2891_CR4 publication-title: Nat Genet doi: 10.1038/ng1180 – volume: 6 start-page: 144 year: 2005 ident: 2891_CR5 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-144 – volume: 21 start-page: 1943 year: 2005 ident: 2891_CR10 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti260 – volume: 27 start-page: 1329 year: 2005 ident: 2891_CR25 publication-title: Int J Oncol – volume: 6 start-page: 71 year: 2007 ident: 2891_CR33 publication-title: Mol Cancer doi: 10.1186/1476-4598-6-71 – volume: 8 start-page: 70 year: 2007 ident: 2891_CR35 publication-title: BMC Genomics doi: 10.1186/1471-2164-8-70 – volume: 20 start-page: 307 year: 2004 ident: 2891_CR52 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg405 – volume: 34 start-page: e151 year: 2006 ident: 2891_CR19 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkl766 – volume: 9 start-page: 467 year: 2008 ident: 2891_CR1 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-467 – ident: 2891_CR47 – volume: 46 start-page: 561 year: 1995 ident: 2891_CR43 publication-title: Annu Rev Psychol doi: 10.1146/annurev.ps.46.020195.003021 – volume: 161 start-page: 911 year: 2003 ident: 2891_CR32 publication-title: J Cell Biol doi: 10.1083/jcb.200211021 – volume: 67 start-page: 3171 year: 2007 ident: 2891_CR50 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-06-4571 – volume: 19 start-page: 494 year: 2007 ident: 2891_CR26 publication-title: Clin Oncol (R Coll Radiol) doi: 10.1016/j.clon.2007.04.002 – volume: 20 start-page: 2618 year: 2004 ident: 2891_CR18 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth293 – volume: 8 start-page: 114 year: 2007 ident: 2891_CR14 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-114 – volume: 24 start-page: 5676 year: 2005 ident: 2891_CR37 publication-title: Oncogene doi: 10.1038/sj.onc.1208920 – volume: 25 start-page: 63 year: 1998 ident: 2891_CR44 publication-title: Journal of Applied Statistics doi: 10.1080/02664769823304 – volume: 33 start-page: W592 year: 2005 ident: 2891_CR7 publication-title: Nucleic Acids Res doi: 10.1093/nar/gki484 – volume: 23 start-page: 1356 year: 2007 ident: 2891_CR20 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm116 – ident: 2891_CR48 – volume: 8 start-page: 431 year: 2007 ident: 2891_CR12 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-431 – volume: 89 start-page: 401 year: 2003 ident: 2891_CR36 publication-title: J Cell Biochem doi: 10.1002/jcb.10515 – ident: 2891_CR23 – volume: 98 start-page: 13790 year: 2001 ident: 2891_CR22 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.191502998 – volume: 8 start-page: 242 year: 2007 ident: 2891_CR9 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-242 – volume: 23 start-page: 980 year: 2007 ident: 2891_CR11 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm051 – volume: 7 start-page: 79 year: 2007 ident: 2891_CR24 publication-title: Nat Rev Cancer doi: 10.1038/nrc2069 – volume: 282 start-page: 110 year: 2003 ident: 2891_CR38 publication-title: Exp Cell Res doi: 10.1016/S0014-4827(02)00013-7 – volume: 33 start-page: e175 year: 2005 ident: 2891_CR53 publication-title: Nucleic Acids Res doi: 10.1093/nar/gni179 – reference: 17352823 - BMC Genomics. 2007;8:70 – reference: 17612399 - BMC Bioinformatics. 2007;8:242 – reference: 15980575 - Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W741-8 – reference: 8197455 - Science. 1994 Jun 3;264(5164):1415-21 – reference: 16211229 - Int J Oncol. 2005 Nov;27(5):1329-39 – reference: 17251915 - Nat Rev Cancer. 2007 Feb;7(2):79-94 – reference: 17086191 - Nature. 2006 Nov 16;444(7117):337-42 – reference: 15941488 - BMC Bioinformatics. 2005;6:144 – reference: 11707567 - Proc Natl Acad Sci U S A. 2001 Nov 20;98(24):13790-5 – reference: 15892165 - J Pathol. 2005 Jul;206(3):366-76 – reference: 18202032 - Brief Bioinform. 2008 May;9(3):189-97 – reference: 12808457 - Nat Genet. 2003 Jul;34(3):267-73 – reference: 12796477 - J Cell Biol. 2003 Jun 9;161(5):911-21 – reference: 17971207 - Mol Cancer. 2007;6:71 – reference: 17903287 - BMC Bioinformatics. 2007;8 Suppl 6:S6 – reference: 12531697 - Exp Cell Res. 2003 Jan 15;282(2):110-20 – reference: 12704803 - J Cell Biochem. 2003 May 15;89(2):401-26 – reference: 17988400 - BMC Bioinformatics. 2007;8:431 – reference: 14960456 - Bioinformatics. 2004 Feb 12;20(3):307-15 – reference: 15040833 - J Bone Miner Res. 2004 Mar;19(3):447-54 – reference: 17676588 - Cancer. 2007 Sep 15;110(6):1313-20 – reference: 17127676 - Bioinformatics. 2007 Feb 1;23(3):306-13 – reference: 17409424 - Cancer Res. 2007 Apr 1;67(7):3171-6 – reference: 17392327 - Bioinformatics. 2007 Jun 1;23(11):1356-62 – reference: 16123801 - Oncogene. 2005 Aug 29;24(37):5676-92 – reference: 12118244 - Nat Med. 2002 Aug;8(8):816-24 – reference: 17303618 - Bioinformatics. 2007 Apr 15;23(8):980-7 – reference: 15130934 - Bioinformatics. 2004 Nov 1;20(16):2618-25 – reference: 12459041 - Mol Cancer. 2002 Nov 12;1:5 – reference: 16174746 - Proc Natl Acad Sci U S A. 2005 Sep 20;102(38):13544-9 – reference: 18980677 - BMC Bioinformatics. 2008;9:467 – reference: 16284200 - Nucleic Acids Res. 2005;33(20):e175 – reference: 15647293 - Bioinformatics. 2005 May 1;21(9):1943-9 – reference: 17130162 - Nucleic Acids Res. 2006;34(22):e151 – reference: 18613966 - BMC Bioinformatics. 2008;9:303 – reference: 12807916 - J Biol Chem. 2003 Sep 5;278(36):33637-44 – reference: 14584895 - J Bone Miner Res. 2003 Oct;18(10):1842-53 – reference: 15980543 - Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W592-5 – reference: 16769149 - Lung Cancer. 2006 Aug;53(2):205-10 – reference: 16199517 - Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 – reference: 17407596 - BMC Bioinformatics. 2007;8:114 – reference: 17513096 - Clin Oncol (R Coll Radiol). 2007 Sep;19(7):494-8 |
| SSID | ssj0017805 |
| Score | 2.5380309 |
| Snippet | Background
Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes... Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has... Background Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes... Abstract Background Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 161 |
| SubjectTerms | Algorithms Applications software Bioinformatics Biomedical and Life Sciences Bone Morphogenetic Protein 6 - genetics Bone Morphogenetic Protein 6 - metabolism Cellular signal transduction Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer Simulation DNA microarrays Gene expression Gene Expression Profiling - methods Gene Regulatory Networks Genetic aspects Humans Life Sciences Lung Neoplasms - genetics Lung Neoplasms - metabolism Microarrays Models, Statistical Oligonucleotide Array Sequence Analysis Reproducibility of Results Research Article Sensitivity and Specificity Signal Transduction Software |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Li9RAEC5kQdSD-Da6ahBBXAiTRz_S3sZlHwp6UBf21vRzdyFkFjPDMv_erqQzThR2L167KyRd_XV1dar6K4B3GjlivLOZMXWekVIXmVC8yHxJtLWe2KLC28hfv7HjE_LllJ5ulfrCnLCBHnhQ3IznTIRTFHW1KYnPg_V0RHDBHdcVUaa3vnktxsNUjB8gU39_ryi8Nhxq6BigrNls04YWqGDFZEPqefv_tc5b29PfqZOb-Ok9uLNqL9X6SjXN1hZ1-ADuR98ynQ9jegi3XPsIbg_VJtePYf9ofnTwMT0biKabdRqD17pxfWPauWUa4HRhzvGXYRrc2RQLFl-pIBq5S57AyeHBz_3jLNZQyAxjYpkFh4gyWzEtLNVUCcNyU6tCUR02Is8q64JHF7wuz2tNfGW9NpVWwUcpcsNKq6unsNMuWvccUpNTxbh1mhSamFzVPlgoyrGoFdLauwRmoyKliQTjWOeikf1Bo2YSVS9R9X0LKxL4sHniciDXuEb2E87NRg5psfuGABYZwSJvAksCb3FmJRJftJhZc6ZWXSc___gu5yVSl9GcsATeRyG_CN9vVLyoELSAXFkTyd2JZFiZZtL9ZgSQxC5MZ2vdYtVhMh3nouQJPBvg9Gf4AmtBlzQBPgHaZNzTnvbivKcFL5nAKHUCeyMkZbRH3TVa3duA9sYpePE_puAl3B0CcngVchd2lr9W7lXw65b6db-EfwNvWkCJ priority: 102 providerName: Directory of Open Access Journals – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3raxQxEA9SEfWD-HZr1UUEsbB2H9lkI4icpQ-F-kE96LeQ57Ww7LW3d9T7781ks3ddLdWvyeyymcxkZnYyv0HojQSMGGt0olSVJjiXWcIEzRKbY6m1xToroBr56Bs5HOOvx-Xxujw6MLC9MrSDflLjWf3-1_nyk1P4j17hK7KTuQM2caGLh9TMIBa66ewUg0YOR3idUwD0fl9rFKj7pOUVbxgYKY_l__eJfclk_XmdcpVTvYtuL5ozsbwQdX3JbO3fR_eCvxmPOgF5gG6Y5iG61XWgXD5Cuwejg70P8aQDn66XcUhoy9r4wbg189iJ2Kk6gd-IsXNxY2hifCEcacAzeYzG-3s_dw-T0FchUYSweeKcpJLogkimS1kKpkiqKpGJUjrjZEmhjfPynCdmaSWxLbSVqpDC-S1ZqkiuZfEEbTTTxjxDsUpLQag2EmcSq1RU1p1aJYVGVwB1byK00zOSqwA6Dr0vau6Dj4pwYD0H1vsRkkXo3eqJsw5w4xraz7A3KzqAyvYD09mEB83jNCXMheGlqVSOberMr8GMMmqoLLBQeYRew85yAMNo4LbNRCzaln_58Z2PcoAzK1NMIvQ2ENmp-34lQvGC4wLgZw0otwaUTlvVYPpVL0AcpuCKW2OmixYu2FHKchqhp504rZfPoD90XkaIDgRtsO7hTHN64qHCc8Igcx2h7V4kea9i13B1eyW0_9yCzf_h3nN0p0vCQfnjFtqYzxbmhfPl5vKlV9HfvkU9pg priority: 102 providerName: Scholars Portal – databaseName: Springer Nature OA Free Journals (WRLC) dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3daxQxEA_SIupD8bOutrqIIBaWbnbzsfHtevRDQR_UQt9CPtvCslfcO8r992Z2c9tbLRVfk8mymUxmJpnJbxB6rwEjxjubGVPlGSk0zoTiOPMF0dZ6YnEJr5G_fmMnp-TLGT2L9x3wFmY9fo8rto-D8szCsaSDy8RwztkMJop1YVk2HeIFgMy_CkLeMmpkdDps_r818JoJ-jM9coiRPkIPFs2VWl6rul4zQ0eP0Vb0H9NJv-BP0D3XPEX3-4qSy2doejw5PvyUnvdg0vUyjQFqXbuuMW3dPA0ic2ku4FowDS5rCkWJr1Ugjfgkz9Hp0eHP6UkW6yRkhjExz4LTQ5ktmRaWaqqEYbmpFFZUB2PjWWld8NqCZ-V5pYkvrdem1Cr4ITg3rLC6fIE2mlnjXqLU5FQxbp0mWBOTq8oHLUQ5FK4C6HqXoP0VI6WJIOJQy6KW3WGiYhJYL4H1XQvDCfo4jLjqATTuoD2AtRnoAPq6awgSIeNOkjxnIhyrqatMQXwezKkjggvuuC6JMkWC3sHKSgC3aCB75lwt2lZ-_vFdTgqAJ6M5YQn6EIn8LPy_UfExQuAC4GGNKHdGlGH3mVH325UASeiClLXGzRYtJMxxLgqeoO1enG6mL6Dec0ETxEeCNpr3uKe5vOigvwsmIBKdoL2VSMqoc9o7uLo3CO0_l-DV_3z5NXrYB9fgWeMO2pj_Wrjd4KPN9Ztue_4GgnEu0w priority: 102 providerName: Springer Nature |
| Title | GAGE: generally applicable gene set enrichment for pathway analysis |
| URI | https://link.springer.com/article/10.1186/1471-2105-10-161 https://www.ncbi.nlm.nih.gov/pubmed/19473525 https://www.proquest.com/docview/67377927 https://pubmed.ncbi.nlm.nih.gov/PMC2696452 https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-10-161 https://doaj.org/article/70693835e8c24f0299e49797e7b34ac2 |
| UnpaywallVersion | publishedVersion |
| Volume | 10 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central Open Access Free customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: KQ8 dateStart: 20000101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: KQ8 dateStart: 20000701 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: ABDBF dateStart: 20000101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: ADMLS dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DIK dateStart: 20000101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RPM dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 8FG dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1471-2105 dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M48 dateStart: 20000701 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: HAS SpringerNature Open Access 2022 customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: AAJSJ dateStart: 20001201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerOpen customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: C6C dateStart: 20000112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3ri9NAEF_OFlE_-H5EzxpEEA_S5rHZTfyWK23PwpVyZ-H8FHY3m14xprUPjvrXu5NsanPKieCXBHYnkJ3MzsxmZn6D0DsOGDGpTCwhAtvCLneskFHHSl3MkyTFieNBNfLpiJxM8PDCvzhA46oWhn8TfDbXoKEAVNzeL0PPyioH6KIgl51FkpabPiAdRylZSx1fClhNB85DTeIr77yBmpPROPpSFBlpkipa-YfHatapAPH_XVXv2arreZS7YOo9dGeTL9j2imXZnr3qP0Dfq5WWaSpf25s1b4sf10Ag_ycrHqL72rk1o1IaH6EDmT9Gt8t2l9snqDuIBr2P5rREus62po6e80wWg-ZKrk0lzzNxCf8sTfVKJnRMvmKKVIOnPEWTfu9z98TSTRwsQUi4tpRH5pPEIzxMfO6zUBBbBMxhPleWMCVeIpVLqdy-lAYcp16ScuFxppwkxxbETbj3DDXyeS5fIFPYPiM0kRw7HAubBalSkT6FrlqAqy8N1Kk-Xiw0wjk02sji4qQTkBhYEwNrihHiGOjD7olFie5xA-0xyMOODnC5i4H5chrrbR5Tm4TqzO_LQLg4tZWtlzikIZWUe5gJ10BvQZpiQN7IIbVnyjarVfzp_CyOXMBO821MDPReE6Vz9f6C6UoJxQUA66pRHtYolWoQtek3ldDGMAX5dLmcb1aQzUdp6FIDPS9F-NfyQ2hG7foGojXhrq27PpPPLgtccpeEECY30FG1DWKtEFc3cPVot1H--gle_gvxK3S3jPxBzeUhaqyXG_laOZBr3kK36AVV16A_aKFmFA3Ph-p-3BuNz9Rol3Rbxa8ZdT3FQUtrkJ8wim46 |
| linkProvider | Unpaywall |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4hUEV7qPqkKbREVaWqSBGJ49hxbykCli1wKCBxs2zHWZCiLCK7Qvvv8eSxJW1F1as9jpLxvJwZfwPwWSNGTGHzwJg0DCjRUSAUj4KCUJ3nBc2jGG8jn5yy0QUdXyaXK0D6uzBNtXufkmwsdaPWKduNnBkN3AGlAc6M8MSzhiVWThnXsmx8Nl7mDhClv09I_mXdwAE1OP1_WuMH7uj3UsllvvQZrM-rG7W4U2X5wCUdvIDnXSzpZ-3mv4QVW72CJ213ycVr2DvMDve_-ZMWWLpc-F2yWpe2GfRrO_Od-FybK_xF6Lvw1ccGxXfKkXZYJW_g4mD_fG8UdD0TAsOYmAUuAEpYHjMt8kQnShgWmlRFKtHO8RQszq2L4FyUVfBU0yLOC21irVxMEoWGkVzHb2G1mlb2HfgmTBTjudU00tSEKi2cRUo4NrFCGHvrwW7PSGk6QHHsa1HK5mCRMomsl8j6ZoRFHnxdrrhpwTQeof2Oe7OkQxjsZmB6O5GdVkkeMuGO2IlNDaFF6FyrpYILbrmOqTLEg0-4sxKBLiqspJmoeV3Lo7OfMiMIVZaElHnwpSMqpu79jeouJjguIDbWgHJrQOk00Qymt3sBkjiF5WuVnc5rLJ7jXBDuwUYrTr8-X2DvZ5J4wAeCNvju4Ux1fdXAgBMmMCvtwU4vkrKzP_UjXN1ZCu0_t-D9_zx5G9ZH5yfH8vjo9McmPG2TbnjdcQtWZ7dz-8HFbjP9sVPWe133Nyw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9QwELZQEdcD4iZQaISQEJWizeHYMW_L0m3LUSGgUt8sn9tKUXbVZFXtv8eTOKEBVMSrPY7i8dgz9sx8g9BrCRgx1uhIqSKOcCqTiAmaRDbFUmuLdZJBNvKXI3JwjD-e5Cf-wa3uo917l2SX0wAoTVUzWWnbbfGCTBJ3pEbustKCaCZw-7mOnW6DCgYzMhu8CIDX37sm_zJqpIpaxP4_z-VLiun3oMnBc3oH3VpXK7G5EGV5STnN76G73qoMp50Y3EfXTPUA3ejqTG4eotn-dH_vXbjoIKbLTejd1rI0bWNYmyZ0gnSmTuGxMHSGbAilii-EI_WoJY_Q8Xzvx-wg8tUTIkUIayJnCuVEZ0QynctcMEViVYhE5NKpIEsybZwt5-wtSwuJbaatVJkUzjpJYkVSLbPHaKtaVuYpClWcC0K1kTiRWMWisO5syimUswJAexOgSc9Irjy0OFS4KHl7xSgIB9ZzYH3bQpIAvR1GrDpYjSto38PaDHQAiN02LM8X3O8vTmPC3GU7N4VKsY2dkjWYUUYNlRkWKg3QK1hZDpAXFcTULMS6rvnh9298mgJoWR5jEqA3nsgu3f8r4VMUHBcAJWtEuT2idHtSjbp3egHi0AWBbJVZrmsIo6OUpTRATzpx-jV9BlWg0zxAdCRoo3mPe6qz0xYQPCUM_NMB2u1FkvuTqL6Cq7uD0P5zCZ79z5d30M2vH-b88-HRp-fodud9g7zHbbTVnK_NC2fENfJlu1N_AquWOgk |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3ri9QwEA_HHqJ-8H1aPbWIIB50t480af22HvdQ8DjUhfNTyHNvsbbrPjjWv95Mm67bU04EvyYTaCaTmUln5jcIvRSAEWO0CqTMwgDHIgpyTqPAxFgoZbCKEqhG_nBCjkf4_Vl6toVO21oY8U2KSeVAQwGouL9Zhl40VQ7QRUHPBlNlmkufkUFklWxgny81rGYE76FtklrvvIe2Ryenwy91kZEjaaOVf1jWsU41iP_vqnrDVl3Oo1wHU2-i68tyylcXvCg27NXhbfS93WmTpvK1v1yIvvxxCQTyf7LiDrrlnFt_2EjjXbSly3voWtPucnUf7R8Njw7e-OMG6bpY-S56LgpdD_pzvfCtPE_kOfyz9O0n-dAx-YJbUgee8gCNDg8-7x8HrolDIAnJF4H1yFKiEiJylYqU55KEMuMRT4W1hIYkSluX0rp9hmYCm0QZIRPBrZMUhZLESiQ7qFdWpX6EfBmmnFClBY4EliHPjFWRKYWuWoCrrz00aA-PSYdwDo02Cla_dDLCgDUMWFOPkMhDr9crpg26xxW0b0Ee1nSAy10PVLMxc9ec0ZDk9s2f6kzG2ITW1muc05xqKhLMZeyhFyBNDJA3SkjtGfPlfM7effrIhjFgp6UhJh565YhMZb9fclcpYbkAYF0dyt0OpVUNsjP9vBVaBlOQT1fqajmHbD5K85h66GEjwr-2n0Mz6jj1EO0Id2ff3Zlycl7jksckhzC5h_baa8CcQpxfwdW99UX56xE8_hfiJ-hGE_mDmstd1FvMlvqpdSAX4pnTCT8BIpNmmw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=GAGE%3A+generally+applicable+gene+set+enrichment+for+pathway+analysis&rft.jtitle=BMC+bioinformatics&rft.au=Luo%2C+Weijun&rft.au=Friedman%2C+Michael+S&rft.au=Shedden%2C+Kerby&rft.au=Hankenson%2C+Kurt+D&rft.date=2009-05-27&rft.pub=BioMed+Central+Ltd&rft.issn=1471-2105&rft.eissn=1471-2105&rft.volume=10&rft.issue=161&rft.spage=161&rft_id=info:doi/10.1186%2F1471-2105-10-161&rft.externalDBID=ISR&rft.externalDocID=A201815046 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |