Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis
Background Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP , CRP and other serum inflammatory...
Saved in:
| Published in | Biomedical engineering online Vol. 18; no. 1; pp. 9 - 12 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
25.01.2019
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1475-925X 1475-925X |
| DOI | 10.1186/s12938-019-0625-6 |
Cover
| Abstract | Background
Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as
BNP
,
CRP
and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI.
Results
Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction.
Conclusions
The results suggested that three overlapping genes (
FGFBP2
,
GFOD1
and
MLC1
) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. |
|---|---|
| AbstractList | Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI.
Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction.
The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. Abstract Background Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI. Results Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction. Conclusions The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. Background Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI. Results Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction. Conclusions The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. Keywords: Acute myocardial infarction, Weighted gene co-expression network analysis, Gene ontology, Functional enrichment analysis Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI. Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction. The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. Background Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI. Results Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction. Conclusions The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. Background Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP , CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI. Results Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction. Conclusions The results suggested that three overlapping genes ( FGFBP2 , GFOD1 and MLC1 ) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI.BACKGROUNDAcute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI.Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction.RESULTSHere, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction.The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI.CONCLUSIONSThe results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI. |
| ArticleNumber | 9 |
| Audience | Academic |
| Author | Liu, Zhihua Gu, Junhua Ma, Chenguang Yu, Ming |
| Author_xml | – sequence: 1 givenname: Zhihua surname: Liu fullname: Liu, Zhihua email: zh.liu@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing Yuqiu Medical Research Institute, Shenzhen Yuqiu Biological Big Data Research Institute, Nanjing Yuqiu Biotechnology Co., Ltd – sequence: 2 givenname: Chenguang surname: Ma fullname: Ma, Chenguang organization: Tsinghua University, Beijing Yuqiu Medical Research Institute, Shenzhen Yuqiu Biological Big Data Research Institute, Nanjing Yuqiu Biotechnology Co., Ltd – sequence: 3 givenname: Junhua surname: Gu fullname: Gu, Junhua organization: Shenzhen Yuqiu Biological Big Data Research Institute, Nanjing Yuqiu Biotechnology Co., Ltd., Hebei University of Technology – sequence: 4 givenname: Ming surname: Yu fullname: Yu, Ming organization: Shenzhen Yuqiu Biological Big Data Research Institute, Nanjing Yuqiu Biotechnology Co., Ltd., Hebei University of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30683112$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv1DAUhSNURB_wA9igSGxgkeJH7CQbpKriMVIlEA-JneU416mnmXhqO7Tz77lhStupAGWRK_s7x77X5zDbG_0IWfackmNKa_kmUtbwuiC0KYhkopCPsgNaVqJomPixd6_ezw5jXBLCCJHNk2yfE1lzStlB5j77BGNyeshb51c6XECIube5NlOCfLXxRodu3naj1cEk58e81RG6HIsrcP15wrqHEXLjC7heB4hxhkZIVz5c5HrUwya6-DR7bPUQ4dnN_yj7_v7dt9OPxdmnD4vTk7PCSF6lomNlbWQprNUaasq5bgwzUrdAjeUNtx2AaZipO0kt8FYSobHuyoqTThrKj7LF1rfzeqnWwWFTG-W1U78XfOiVDsmZAZRE85o2ghAhSkHbpoOyNRWtS04s5xa93m691lO7gs7gpIIedkx3d0Z3rnr_E53LqiolGry6MQj-coKY1MpFA8OgR_BTVIxWTcmlIBWiLx-gSz8FHN5M4WsJTip5R_UaG8A38XiumU3ViahpyWpW1Ugd_4XCr4OVMxgi63B9R_B6R4BMguvU6ylGtfj6ZZd9cX8ot9P4EyoE6BYwwccYwN4ilKg5uGobXIXBVXNw1dxX9UBjXNJz2vDmbvivkm2VEU8Zewh3c_u36BeywQFU |
| CitedBy_id | crossref_primary_10_1371_journal_pone_0264362 crossref_primary_10_1097_MD_0000000000033634 crossref_primary_10_3389_fcvm_2020_601364 crossref_primary_10_1089_dna_2020_6447 crossref_primary_10_1016_j_bbrc_2024_149906 crossref_primary_10_3724_abbs_2024105 crossref_primary_10_3892_etm_2021_11085 crossref_primary_10_1155_2021_5584681 crossref_primary_10_1093_bib_bbab602 crossref_primary_10_1016_j_gep_2020_119111 crossref_primary_10_1186_s12917_023_03789_9 crossref_primary_10_3892_etm_2022_11270 crossref_primary_10_1080_21655979_2021_2003932 crossref_primary_10_3389_fcvm_2024_1360548 crossref_primary_10_1590_1414_431x2023e12910 crossref_primary_10_1007_s13721_022_00371_5 crossref_primary_10_1631_jzus_B2000544 crossref_primary_10_3389_fcvm_2022_912454 crossref_primary_10_1016_j_heliyon_2023_e13323 crossref_primary_10_1186_s12891_021_03958_7 crossref_primary_10_3390_genes13122321 crossref_primary_10_3390_jcdd9010030 |
| Cites_doi | 10.1097/HJH.0b013e3282ef9819 10.1038/srep43167 10.1111/j.1365-2796.2012.02589.x 10.1186/s13040-016-0117-1 10.1093/bioinformatics/bts666 10.1016/j.artmed.2017.02.005 10.1182/blood-2004-08-3283 10.1371/journal.pone.0042076 10.1007/s11033-012-2330-4 10.1007/s10741-007-9004-7 10.1093/molbev/mst021 10.1016/j.jjcc.2010.10.003 10.1016/j.amjcard.2007.02.077 10.1159/000258197 10.1186/gm560 10.1001/archinte.167.6.573 10.1016/j.jacc.2006.05.055 10.1038/s41598-017-04037-5 10.1016/j.yjmcc.2014.04.017 10.1001/archinte.162.21.2405 10.1002/path.2652 10.1186/1755-8794-4-83 10.1038/nature03985 10.5812/ircmj.26919v2 10.1007/s10741-016-9554-7 10.1016/j.ijcard.2015.03.305 10.1161/ATVBAHA.114.304405 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2019 COPYRIGHT 2019 BioMed Central Ltd. Copyright © 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2019 – notice: COPYRIGHT 2019 BioMed Central Ltd. – notice: Copyright © 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. L6V LK8 M0S M1P M7P M7S P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7X8 5PM DOA |
| DOI | 10.1186/s12938-019-0625-6 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection Biological Sciences Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Engineering Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection ProQuest Engineering Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals 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: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1475-925X |
| EndPage | 12 |
| ExternalDocumentID | oai_doaj_org_article_63a981950055451b9de4bc718430f33f PMC6347746 A581428278 30683112 10_1186_s12938_019_0625_6 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: SZ Basic Research Grant grantid: JCYJ20150401145529007 – fundername: SZ Technology Research Grant grantid: CXZZ20151015163619907; JSGG20160229123927512; KQJSCX20170331161941176 – fundername: SZ Technology Research Grant grantid: KQJSCX20170331161941176 – fundername: SZ Technology Research Grant grantid: CXZZ20151015163619907 – fundername: SZ Technology Research Grant grantid: JSGG20160229123927512 – fundername: ; grantid: JCYJ20150401145529007 – fundername: ; grantid: CXZZ20151015163619907; JSGG20160229123927512; KQJSCX20170331161941176 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5GY 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABJCF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FRP FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE I-F IAO IGS IHR INH INR ISR ITC KQ8 L6V LK8 M1P M48 M7P M7S MK~ ML~ M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PUEGO RBZ RNS ROL RPM RSV SEG SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX ALIPV CITATION CGR CUY CVF ECM EIF NPM PMFND 3V. 7QO 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c637t-d248c645ffaae8133a9c2c6abe1cf393fdeec92c8d61fe3b605a8d6d4730d6c13 |
| IEDL.DBID | M48 |
| ISSN | 1475-925X |
| IngestDate | Wed Aug 27 01:31:09 EDT 2025 Thu Aug 21 14:26:58 EDT 2025 Thu Sep 04 18:09:45 EDT 2025 Fri Jul 25 19:33:09 EDT 2025 Tue Jun 17 21:46:32 EDT 2025 Tue Jun 10 20:29:10 EDT 2025 Fri Jun 27 04:22:33 EDT 2025 Thu Apr 03 07:04:31 EDT 2025 Tue Jul 01 00:34:37 EDT 2025 Thu Apr 24 23:07:17 EDT 2025 Sat Sep 06 07:30:06 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Gene ontology Functional enrichment analysis Acute myocardial infarction Weighted gene co-expression network analysis |
| Language | English |
| License | Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c637t-d248c645ffaae8133a9c2c6abe1cf393fdeec92c8d61fe3b605a8d6d4730d6c13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/s12938-019-0625-6 |
| PMID | 30683112 |
| PQID | 2183153076 |
| PQPubID | 42562 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_63a981950055451b9de4bc718430f33f pubmedcentral_primary_oai_pubmedcentral_nih_gov_6347746 proquest_miscellaneous_2179436507 proquest_journals_2183153076 gale_infotracmisc_A581428278 gale_infotracacademiconefile_A581428278 gale_incontextgauss_ISR_A581428278 pubmed_primary_30683112 crossref_primary_10_1186_s12938_019_0625_6 crossref_citationtrail_10_1186_s12938_019_0625_6 springer_journals_10_1186_s12938_019_0625_6 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2019-01-25 |
| PublicationDateYYYYMMDD | 2019-01-25 |
| PublicationDate_xml | – month: 01 year: 2019 text: 2019-01-25 day: 25 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Biomedical engineering online |
| PublicationTitleAbbrev | BioMed Eng OnLine |
| PublicationTitleAlternate | Biomed Eng Online |
| PublicationYear | 2019 |
| Publisher | BioMed Central BioMed Central Ltd BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: BMC |
| References | Y Ge (625_CR5) 2012; 272 D Mozaffarian (625_CR1) 2016; 133 A Bauters (625_CR24) 2007; 100 ZH Liu (625_CR10) 2012; 29 DV Cokkinos (625_CR23) 2016; 21 UA Ajani (625_CR3) 2006; 48 J Kim (625_CR9) 2014; 6 S Aoki (625_CR25) 2011; 57 Y Devaux (625_CR8) 2011; 4 ZH Liu (625_CR14) 2013; 40 R Suresh (625_CR19) 2014; 74 PA Hall (625_CR6) 2010; 220 D Yang (625_CR12) 2012; 7 L Wei (625_CR15) 2017; 83 J Li (625_CR17) 2017; 7 SE Calvano (625_CR20) 2005; 437 B Heidecker (625_CR7) 2007; 12 F Abdulaziz Qari (625_CR22) 2015; 17 M Hausberg (625_CR28) 2007; 25 SB Wettinger (625_CR21) 2005; 105 J Li (625_CR18) 2016; 9 D Wang (625_CR16) 2017; 7 GM Ren (625_CR11) 2013; 29 H Jiang (625_CR26) 2015; 35 MR Law (625_CR2) 2002; 162 S Ozturk (625_CR27) 2015; 186 A Hozawa (625_CR4) 2007; 167 JK Cheng (625_CR13) 2013; 30 |
| References_xml | – volume: 25 start-page: 2004 issue: 10 year: 2007 ident: 625_CR28 publication-title: J Hypertens doi: 10.1097/HJH.0b013e3282ef9819 – volume: 7 start-page: 43167 year: 2017 ident: 625_CR16 publication-title: Sci Rep doi: 10.1038/srep43167 – volume: 272 start-page: 430 issue: 5 year: 2012 ident: 625_CR5 publication-title: J Intern Med doi: 10.1111/j.1365-2796.2012.02589.x – volume: 9 start-page: 37 year: 2016 ident: 625_CR18 publication-title: BioData Min doi: 10.1186/s13040-016-0117-1 – volume: 29 start-page: 279 issue: 2 year: 2013 ident: 625_CR11 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts666 – volume: 83 start-page: 82 year: 2017 ident: 625_CR15 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2017.02.005 – volume: 105 start-page: 2000 issue: 5 year: 2005 ident: 625_CR21 publication-title: Blood doi: 10.1182/blood-2004-08-3283 – volume: 7 start-page: e42076 issue: 7 year: 2012 ident: 625_CR12 publication-title: PLoS ONE doi: 10.1371/journal.pone.0042076 – volume: 40 start-page: 2491 year: 2013 ident: 625_CR14 publication-title: Mol Biol Rep doi: 10.1007/s11033-012-2330-4 – volume: 12 start-page: 1 issue: 1 year: 2007 ident: 625_CR7 publication-title: Heart Fail Rev doi: 10.1007/s10741-007-9004-7 – volume: 30 start-page: 1032 issue: 5 year: 2013 ident: 625_CR13 publication-title: Mol Biol Evol doi: 10.1093/molbev/mst021 – volume: 57 start-page: 202 issue: 2 year: 2011 ident: 625_CR25 publication-title: J Cardiol doi: 10.1016/j.jjcc.2010.10.003 – volume: 100 start-page: 182 issue: 2 year: 2007 ident: 625_CR24 publication-title: Am J Cardiol doi: 10.1016/j.amjcard.2007.02.077 – volume: 29 start-page: 851 year: 2012 ident: 625_CR10 publication-title: Cell Physiol Biochem doi: 10.1159/000258197 – volume: 6 start-page: 40 issue: 5 year: 2014 ident: 625_CR9 publication-title: Genome Med doi: 10.1186/gm560 – volume: 167 start-page: 573 issue: 6 year: 2007 ident: 625_CR4 publication-title: Arch Intern Med doi: 10.1001/archinte.167.6.573 – volume: 48 start-page: 1177 issue: 6 year: 2006 ident: 625_CR3 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2006.05.055 – volume: 7 start-page: 4354 year: 2017 ident: 625_CR17 publication-title: Sci Rep doi: 10.1038/s41598-017-04037-5 – volume: 74 start-page: 13 year: 2014 ident: 625_CR19 publication-title: J Mol Cell Cardiol doi: 10.1016/j.yjmcc.2014.04.017 – volume: 133 start-page: e38 issue: 4 year: 2016 ident: 625_CR1 publication-title: Circulation – volume: 162 start-page: 2405 issue: 21 year: 2002 ident: 625_CR2 publication-title: Arch Intern Med doi: 10.1001/archinte.162.21.2405 – volume: 220 start-page: 109 issue: 2 year: 2010 ident: 625_CR6 publication-title: J Pathol doi: 10.1002/path.2652 – volume: 4 start-page: 83 year: 2011 ident: 625_CR8 publication-title: BMC Med Genom doi: 10.1186/1755-8794-4-83 – volume: 437 start-page: 1032 issue: 7061 year: 2005 ident: 625_CR20 publication-title: Nature doi: 10.1038/nature03985 – volume: 17 start-page: e26919 issue: 7 year: 2015 ident: 625_CR22 publication-title: Iran Red Crescent Med J doi: 10.5812/ircmj.26919v2 – volume: 21 start-page: 365 issue: 4 year: 2016 ident: 625_CR23 publication-title: Heart Fail Rev doi: 10.1007/s10741-016-9554-7 – volume: 186 start-page: 299 year: 2015 ident: 625_CR27 publication-title: Int J Cardiol doi: 10.1016/j.ijcard.2015.03.305 – volume: 35 start-page: 580 issue: 3 year: 2015 ident: 625_CR26 publication-title: Arterioscler Thromb Vasc Biol doi: 10.1161/ATVBAHA.114.304405 |
| SSID | ssj0020069 |
| Score | 2.3295338 |
| Snippet | Background
Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis... Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify... Background Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis... Abstract Background Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 9 |
| SubjectTerms | Acute Coronary Syndrome - metabolism Acute myocardial infarction Bioinformatics Biological markers Biomarkers Biomarkers - metabolism Biomaterials Biomedical Engineering and Bioengineering BioMedical Engineering and the Heart Biomedical Engineering/Biotechnology Biotechnology Blood Blood Proteins - metabolism Cardiovascular disease Case-Control Studies Cluster Analysis Computational Biology Congestive heart failure Coronary vessels Datasets Developed countries Diabetes Mellitus, Type 1 - metabolism Diagnosis DNA microarrays Engineering Enrichment Feasibility studies Functional enrichment analysis Gene expression Gene Expression Profiling Gene Expression Regulation Gene ontology Genes Genetic aspects Genomes Health aspects Health care Heart Heart attack Heart attacks Heart failure Humans Immune response Immune system Industrialized nations Inflammation Membrane Proteins - metabolism Modules Mortality Myocardial infarction Myocardial Infarction - genetics Myocardial Infarction - metabolism Network analysis Oligonucleotide Array Sequence Analysis Ontology Oxidoreductases - genetics Oxidoreductases - metabolism Patient Discharge Patients Pilot Projects Renal failure Thyroid Thyroid diseases Thyroid Diseases - metabolism Transcription, Genetic Weighted gene co-expression network analysis |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3da9UwFA-yB9EH2eZX55QogqCEtU3z0ccpjilMRB3sLSRpooOtHWsv6n-_c9rc6-1EffEtNCf3Nuf8kpPTnPxCyPNauTxEUTAlLAQowmnmouYsd8pp7QL4JPw0cPRBHh5X70_EydpVX5gTNtEDT4rbk9zWuNeDZFGVKFzdhMp5hdeU5JHziLMvuLFlMJVCLSTgTXuYhZZ7PXo1TNrCE_elYHLmhUay_t-n5DWfdD1f8tqm6eiLDjbJnbSIpPvTy2-RG6HdJrfXqAW3yc2jtGl-l5x-7AbMCYIWeNge83Eue9pFav1iCPT8J7gzhMkZBbQB7tFUFL1bQ6Hwffx2CmVAWqC-Y-FHyp1taTvlkFObmE3ukeODt1_eHLJ0wwLzkquBNWWlvaxEjNYGDeGqrX3ppXWh8JHXPDYh-Lr0upFFDNxB7GOh3FQwLzTSF_w-2Wi7NjwktLFaCIc3_EWI0XRlPXe84T4Uwgqd-4zkS40bn-jH8RaMMzOGIVqayUgGjGTQSEZm5OWqycXEvfE34ddoxpUg0maPDwBMJoHJ_AtMGXmGIDBIjNFi5s1Xu-h78-7zJ7MvNJLTlUpn5EUSih30wNt0kAH0gFxaM8ndmSSMXD-vXmLNpJmjN7hkBS-UK-jR01U1tsRsuDZ0C5RBWj9YW6uMPJigueo3hIDwA0WZETUD7Uwx85r29NvIKy55BcEA_O-rJbx_vdYf9b7zP_T-iNwqx8FZsFLsko3hchEew2JvcE_GcX0F2bVOXA priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3ha9QwFA86QfSD6NRZnRJFEJSypmnS9JNM8ZzCRNTBvoUkTeZgtvPaQ_3vfa_NdevEfQuXl7tL3i95ecnL7xHyvCpt5oNgaSkMOCjCqtQGxdPMllYp68Em4dHA_ie5d1B8PBSH8cCti2GV6zVxWKjr1uEZ-Q6acpid4Ha_Pv2ZYtYovF2NKTSukmssByThS_HF-8nhQhreeJPJlNzp0LZh6Ba-u89FKme2aKDs_3dhPmeZLkZNXrg6HSzS4ja5FbeSdHfU_R1yxTeb5OY5gsFNcn0_Xp3fJcef2x4jg6AFPrnHqJxlR9tAjVv1nv74A0YNwXJCAXOAflQYRRtXUyj8Gk5QoQx489S1qf8dI2gb2oyR5NREfpN75GDx7tvbvTTmWUid5GWf1nmhnCxECMZ4BU6rqVzupLGeucArHmrvXZU7VUsWPLfgARko1wWsDrV0jN8nG03b-AeE1kYJYTHPXwBPTRXGcctr7jwTRqjMJSRbj7h2kYQcc2Gc6MEZUVKPStKgJI1K0jIhL6cmpyMDx2XCb1CNkyCSZw8ftMsjHeeiltBBvD5E_rFCMFvVvrCuxMw3WeA8JOQZgkAjPUaD8TdHZtV1-sPXL3pXKKSoy0uVkBdRKLTQA2ficwYYB2TUmkluzyRh_rp59RprOq4fnT5De0KeTtXYEmPiGt-uUAbJ_WCHXSZka4Tm1G9wBOELWJ6Qcgba2cDMa5rj7wO7uOQFuATwu6_W8D77W_8d94eXd-IRuZEP046ludgmG_1y5R_DZq63T4YZ-xfntEcx priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3di9QwEB_0BPEeRM-v6ilRBEEptk2Tpo_n4nEKJ6Ie3FtI0kQPzva4dlH_e2fa7Lo9P8C3sJm0m8wvmZlm8gvA07qymQ8iTythMEARVqU2KJ5mtrJKWY82iT4NHL6TB0fl22NxHMmi6SzM5v59ruTLnuwRpVvRWflCpPIyXBG47lL23kIu1rEVMe7GTcs_NpuZnZGd__c1eMMIXUyQvLBLOhqf_RtwPXqNbG9S80245Nsd2N7gEtyBq4dxl_wWnLzvBkoCwhZ0up4ScM571gVm3HLw7OsPtF-Ei1OG8EKgk24YmbOGYeHb-LEUywgtz1yX-u8xWbZl7ZQ0zkykMrkNR_uvPy0O0nilQuokr4a0KUrlZClCMMYrjE9N7QonjfW5C7zmofHe1YVTjcyD5xaDHYPlpsSFoJEu53dgq-1afw9YY5QQlq70CxiUqdI4bnnDnc-FESpzCWSrEdcu8o3TtReneow7lNSTkjQqSZOStEzg-brJ2US28S_hV6TGtSDxZI8_IHx0nHZaYgdpp5CoxkqR27rxpXUVXXKTBc5DAk8IBJqYMFpKtflsln2v33z8oPeEIja6olIJPItCocMeOBNPLuA4EHnWTHJ3JolT1c2rV1jTcanoNfmoaHayCnv0eF1NLSn9rfXdkmSIxw-d6SqBuxM01_3GmA8fkBcJVDPQzgZmXtOefBmJxCUv0fvH975YwfvX3_rruN__L-kHcK0YZ2GeFmIXtobzpX-IbtxgH40T-CfYDD_B priority: 102 providerName: Springer Nature |
| Title | Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis |
| URI | https://link.springer.com/article/10.1186/s12938-019-0625-6 https://www.ncbi.nlm.nih.gov/pubmed/30683112 https://www.proquest.com/docview/2183153076 https://www.proquest.com/docview/2179436507 https://pubmed.ncbi.nlm.nih.gov/PMC6347746 https://doaj.org/article/63a981950055451b9de4bc718430f33f |
| Volume | 18 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: RBZ dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: KQ8 dateStart: 20020101 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: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: KQ8 dateStart: 20020501 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: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: DOA dateStart: 20020101 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: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: ABDBF dateStart: 20020101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: ADMLS dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: DIK dateStart: 20020101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: GX1 dateStart: 20020101 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: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: M~E dateStart: 20020101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: RPM dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X 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: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: 8FG dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1475-925X dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: M48 dateStart: 20020501 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: AAJSJ dateStart: 20021201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: C6C dateStart: 20021201 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3rb9MwELf2kBB8QDBegVEFhIQECiRx_MgHhLZqZSB1mgaV-s2yHXtMKgn0Ibb_njs3LcsYiC-VG5-T-HyX89nn3xHyohQmdZ5liWAaHBRmZGK8pElqhJHSOLBJuDQwPOKHo-LTmI03yCq9VcvA2bWuHeaTGk0nb85_XLwHhX8XFF7ytzO0WRiShefpc5bwTbINhilHIR8W600FdJ7LcNhIsKTM2bjd5Lz2Fh0zFdD8__xmXzJaVwMqr-yqBmM1uENut7PMeG8pFnfJhqt3yK1L2IM75Maw3VW_R86OmzkGDUELPI2PATvTWdz4WNvF3MXfLsDeoRxNYmAQKAaOZYzmr4qh8DMsrkIZRNHFtknceRtcW8f1Msg81i30yX0yGhx86R8mbQqGxHIq5kmVF9LygnmvtZPgz-rS5pZr4zLraUl95ZwtcysrnnlHDThHGspVAR-OituMPiBbdVO7RySutGTMYApAD06cLLSlhlbUuoxpJlMbkXTFcWVbfHJMkzFRwU-RXC0HScEgKRwkxSPyat3k-xKc41_E-ziMa0LE1Q4XmumpatVUcegg7iwiNFnBMlNWrjBWYFKc1FPqI_IchUAhckaNoTmnejGbqY-fT9Qek4helwsZkZctkW-gB1a3Jx2ADwi21aHc7VCCattu9UrW1EozFM5pwUylAnr0bF2NLTFcrnbNAmkQ9w8m3yIiD5eiue43-IhwgyyPiOgIbYcx3Zr67GsAHue0AG8Bnvt6Jd6_X-uvfH_839x4Qm7mQQOzJGe7ZGs-XbinMOWbmx7ZFGMBv3LwoUe29w-Ojk_gX5_3e2ERpRcU_RdKOFWn |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Zb9QwELZKkTgeEJQrUMAgEFJR1CSOHe8DQuWodukhBK20b8Z27FKpJGWzq9I_xW9kJsduU0Tf-mbF4yT2zHg89vgbQl4OMhM5z-Mw4xocFG5kaLxkYWQyI6VxYJNwa2BnVwz3089jPl4if7q7MBhW2c2J9USdlxb3yNfRlIN2gtv97vhXiFmj8HS1S6HRiMWWOz0Bl616O_oI_H2VJJuf9j4MwzarQGgFy6ZhnqTSipR7r7WT4KLpgU2s0MbF1rMB87lzdpBYmYvYO2Zgva-hnKegC7mwMYP3XiFXUxaliNWfjRcOHsL-tiensRTrFdpSDBXDe_4JD0XP9tUpAv41BGcs4fkozXNHtbUF3LxNbrVLV7rRyNodsuSKFXLzDKDhCrm20x7V3yWHX8opRiJBC7zij1FAk4qWnmo7mzr68xSMKArnEQUZh2FFAaFoU3MKhZN6xxbKIN-O2jJ0v9uI3YIWTeQ61S2eyj2yfykcuE-Wi7JwDwnNteTcYF5BD56hTLVlhuXMuphrLiMbkKgbcWVb0HPMvXGkaudHCtUwSQGTFDJJiYCszZscN4gfFxG_RzbOCRGsu35QTg5Uq_tKQAfxuBLxzlIem0HuUmMzzLQTecZ8QF6gECiE4ygw3udAz6pKjb59VRtcIiReksmAvG6JfAk9sLq9PgHjgAhePcrVHiXMF7Zf3cmaauerSi20KyDP59XYEmPwClfOkAbBBGFFnwXkQSOa836D4wkviJOAZD2h7Q1Mv6Y4_FGjmQuWggsC333Tiffit_477o8u7sQzcn24t7Ottke7W4_JjaRWwThM-CpZnk5m7gksJKfmaa29lHy_7OniL4evhh8 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9QwDI9gkyZ4QDBgFAYEhIQEqtY2TZo-Hh-n7WDTxJi0tyhJkzFptNP1TsB_j93myjo-JN6ii9OeYzuOa-cXQl6UhUmc52lccA0BCjcyNl6yODGFkdI48En4aWD_QOwe57MTfhLuOW1X1e6rlGR_pgFRmurFzkXlexOXYqdFL4VFWHiCPuOxuE7WJS8FRF_rk8nsaDbEXIjEG5KZfxw4ckcdav_va_Ml53S1cPJK9rRzStPb5FbYTdJJL_475JqrN8nNSxiDm2RjP2TP75KzwwZ5BI2jeOoeC3PmLW081Xa5cPTrD_BrqC_nFNQODABlRtHNVRQa37qPqNAGlXPUNrH7Hopoa1r3xeRUB4iTe-R4-v7z2904XLUQW8GKRVxlubQi595r7STErbq0mRXauNR6VjJfOWfLzMpKpN4xA0GQhnaVwwJRCZuy-2Stbmr3gNBKS84NXvXnIViTubbMsIpZl3LNZWIjkqxmXNmAQ47XYZyrLh6RQvVCUiAkhUJSIiKvhiEXPQjHv4jfoBgHQsTP7n5o5qcqmKMSwCBmEBGCLOepKSuXG1vg5TeJZ8xH5DkqgUKEjBpLcE71sm3V3tEnNeESUeqyQkbkZSDyDXBgdTjRAPOAoFojyu0RJZiwHXevdE2FJaRVuHcFd5QUwNGzoRtHYllc7Zol0iC-H2yyi4hs9ao58A2xIDwgzSJSjJR2NDHjnvrsSwcwLlgOUQG89_VKvX_9rb_O-8P_on5KNg7fTdXHvYMPj8iNrDPINM74NllbzJfuMez0FuZJsOafqpNMbA |
| 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=Potential+biomarkers+of+acute+myocardial+infarction+based+on+weighted+gene+co-expression+network+analysis&rft.jtitle=Biomedical+engineering+online&rft.au=Liu%2C+Zhihua&rft.au=Ma%2C+Chenguang&rft.au=Gu%2C+Junhua&rft.au=Yu%2C+Ming&rft.date=2019-01-25&rft.pub=BioMed+Central+Ltd&rft.issn=1475-925X&rft.eissn=1475-925X&rft.volume=18&rft.issue=1&rft_id=info:doi/10.1186%2Fs12938-019-0625-6&rft.externalDocID=A581428278 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1475-925X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1475-925X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1475-925X&client=summon |