Deep learning-based approaches for multi-omics data integration and analysis
Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged...
Saved in:
| Published in | BioData mining Vol. 17; no. 1; pp. 38 - 29 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
02.10.2024
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1756-0381 1756-0381 |
| DOI | 10.1186/s13040-024-00391-z |
Cover
| Abstract | Background
The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration.
Method
In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration.
Results
Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data.
Conclusion
We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. |
|---|---|
| AbstractList | Abstract Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. Method In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Results Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. Conclusion We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration.BACKGROUNDThe rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration.In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration.METHODIn this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration.Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data.RESULTSDeep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data.We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample.CONCLUSIONWe expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. Method In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Results Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. Conclusion We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. Keywords: Deep learning, Generative model, Multi-omics integration, Imaging The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. Method In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Results Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. Conclusion We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. BackgroundThe rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration.MethodIn this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration.ResultsDeep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data.ConclusionWe expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. |
| ArticleNumber | 38 |
| Audience | Academic |
| Author | Ballard, Jenna L. Wang, Zexuan Shen, Li Long, Qi Li, Wenrui |
| Author_xml | – sequence: 1 givenname: Jenna L. surname: Ballard fullname: Ballard, Jenna L. email: jenna.ballard@pennmedicine.upenn.edu organization: Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania – sequence: 2 givenname: Zexuan surname: Wang fullname: Wang, Zexuan organization: Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania – sequence: 3 givenname: Wenrui surname: Li fullname: Li, Wenrui organization: Department of Statistics, University of Connecticut – sequence: 4 givenname: Li surname: Shen fullname: Shen, Li email: li.shen@pennmedicine.upenn.edu organization: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania – sequence: 5 givenname: Qi surname: Long fullname: Long, Qi email: qlong@pennmedicine.upenn.edu organization: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39358793$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkktv1DAUhSNURNuBP8ACRWIDixQ_YidZoaq8RhoJicfaunFuUo8ydrATYPrrcSdD26lQhbywZX_n2Pf4niZH1llMkueUnFFayjeBcpKTjLA8I4RXNLt6lJzQQsiM8JIe3VkfJ6chrAmRjAj-JDnmFRdlUfGTZPUOcUh7BG-N7bIaAjYpDIN3oC8xpK3z6WbqR5O5jdEhbWCE1NgROw-jcTYFG3kL_TaY8DR53EIf8Nl-XiTfP7z_dvEpW33-uLw4X2Va5mzMOGONKEpGgMiGYIsF8LKpKZeaIgDypmBEVgwKaAsthaAiVlIJIQrMJan4IlnOvo2DtRq82YDfKgdG7Tac7xT40egeFW8Ea0tKNJUirxtRF5xHs5pRXletFNGLz16THWD7C_r-xpASdZ2zmnNWMWe1y1ldRdXbWTVM9QYbjXb00B885fDEmkvVuZ_RMI8lkDw6vNo7ePdjwjCqjQka-x4suikoTikTrMwFj-jLe-jaTT5mvqMEjXlKckt1EOs2tnXxYn1tqs5LSgkjeUQXydk_qDgajP8b-6s1cf9A8PpAEJkRf48dTCGo5dcvh-yLu6ncxPG33SLAZkB7F4LH9v-yLu-JtBl3zRefbvqHpfvPDfEe26G_Te4B1R87kgVX |
| CitedBy_id | crossref_primary_10_3389_fbinf_2024_1546680 crossref_primary_10_3389_fbinf_2024_1510352 crossref_primary_10_3390_cancers17060977 crossref_primary_10_1111_jipb_13879 crossref_primary_10_1016_j_patter_2025_101203 crossref_primary_10_1021_acssynbio_4c00864 |
| Cites_doi | 10.1177/15353702211065010 10.1038/s41590-018-0121-3 10.1177/1177932219899051 10.3389/fgene.2022.854752 10.1093/bib/bbab600 10.1186/s13040-020-00222-x 10.3389/fgene.2019.00617 10.1093/bioinformatics/btab403 10.1016/j.media.2021.102266 10.1093/bioinformatics/btab109 10.1093/nar/gkz757 10.1016/j.joca.2008.06.016 10.1038/ng.2529 10.1093/nar/30.1.42 10.1093/bib/bbab454 10.1038/nm.3954 10.1038/s41556-022-00932-w 10.1186/s13059-015-0694-1 10.1038/nbt.3192 10.1371/journal.pcbi.1006376 10.1186/s12864-019-6285-x 10.1016/j.cell.2016.06.017 10.1109/TCBB.2019.2909905 10.1007/978-3-030-20242-2_10 10.1038/sdata.2018.202 10.3390/jpm12040601 10.1016/j.ymeth.2019.03.004 10.1016/j.csbj.2021.08.006 10.1038/s41746-022-00742-2 10.1200/CCI.19.00126 10.1038/s41587-022-01284-4 10.1093/bioinformatics/btw344 10.1016/j.cell.2020.09.056 10.1017/S1041610209009405 10.3109/09638237.2015.1124385 10.1101/531327 10.1093/nar/gki072 10.3389/fgene.2022.806842 10.1093/nar/gky1131 10.1016/j.media.2022.102643 10.3389/fgene.2018.00477 10.1093/bioinformatics/btab608 10.1109/TMI.2020.3021387 10.1186/s13040-023-00349-7 10.1101/114892 10.1101/2022.09.05.506572 10.3390/cancers13123047 10.1093/bioinformatics/btx682 10.1038/ng.2764 10.1109/JBHI.2021.3102186 10.1038/s41588-020-0696-0 10.1038/s41467-021-22368-w 10.1038/s41586-019-1186-3 10.1101/2021.03.02.433454 10.1038/sdata.2016.15 10.1109/CSCI51800.2020.00144 10.7554/eLife.05005 10.1109/TMI.2014.2377694 10.1038/s41586-021-03500-8 10.1038/s41587-019-0290-0 10.1080/14786440109462720 10.1038/s41467-021-23774-w 10.1155/2022/5131170 10.1016/j.ymeth.2020.07.008 10.1186/s12911-020-01225-8 10.3389/fgene.2019.00166 10.1101/2023.04.30.538439 10.1109/BIBM47256.2019.8983228 10.1002/aisy.202200247 10.1016/j.eswa.2023.120761 10.1101/2021.01.25.427845 10.1101/2022.03.16.484643 10.3389/frai.2023.1098308 10.1093/bioinformatics/btac080 10.1101/2019.12.13.19014902 10.1038/nature11003 10.1016/j.isci.2023.107378 10.1016/j.csbj.2021.04.067 10.1109/TMI.2018.2868977 10.3389/fgene.2020.564792 10.1186/s13059-021-02556-z 10.3389/fgene.2022.855629 10.1038/s41598-021-85285-4 10.1038/s41598-020-70229-1 10.3389/fgene.2023.1199087 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). COPYRIGHT 2024 BioMed Central Ltd. 2024. 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. The Author(s) 2024 2024 |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: COPYRIGHT 2024 BioMed Central Ltd. – notice: 2024. 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. – notice: The Author(s) 2024 2024 |
| DBID | C6C AAYXX CITATION NPM ISR 3V. 7QO 7SC 7X7 7XB 8AL 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/s13040-024-00391-z |
| DatabaseName | Springer Nature Link CrossRef PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) Public Health Database 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) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Health & Medical Collection Biological Science Database (Proquest) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic 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 ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Public Health ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature Link 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: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 5 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1756-0381 |
| EndPage | 29 |
| ExternalDocumentID | oai_doaj_org_article_3d52f810c1654bd5b733175b213b9f65 10.1186/s13040-024-00391-z PMC11446004 A811020451 39358793 10_1186_s13040_024_00391_z |
| Genre | Journal Article Review |
| GrantInformation_xml | – fundername: National Institutes of Health grantid: R01 AG071470, U01 AG068057, U01 AG066833, RF1 AG068191, R01 LM013463; RF1 AG063481, R01 AG071174, U01 CA274576 – fundername: NIA NIH HHS grantid: U01 AG068057 – fundername: NIA NIH HHS grantid: U01 AG066833 – fundername: NIH HHS grantid: RF1 AG063481, R01 AG071174, U01 CA274576 – fundername: NLM NIH HHS grantid: R01 LM013463 – fundername: NIA NIH HHS grantid: R01 AG071470 – fundername: NIH HHS grantid: R01 AG071470, U01 AG068057, U01 AG066833, RF1 AG068191, R01 LM013463 |
| GroupedDBID | --- 0R~ 23N 2WC 5GY 5VS 6J9 7X7 8C1 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 AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DWQXO E3Z EBD EBLON EBS ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IEA IHR ISR ITC K6V K7- KQ8 LK8 M48 M7P ML~ M~E O5R O5S OK1 P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PUEGO RBZ RNS ROL RPM RSV SBL SOJ TR2 TUS UKHRP ~8M AAYXX CITATION ALIPV NPM 3V. 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D M0N P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM 2VQ 4.4 ADTOC AHSBF C1A EJD H13 IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c642t-322d57820a06d0efe7a38db136c1eaae3d720692a7af7c6551517595557e46093 |
| IEDL.DBID | M48 |
| ISSN | 1756-0381 |
| IngestDate | Tue Oct 14 19:06:21 EDT 2025 Sun Oct 26 02:18:23 EDT 2025 Tue Sep 30 17:07:15 EDT 2025 Thu Oct 02 11:44:19 EDT 2025 Tue Oct 07 05:58:30 EDT 2025 Mon Oct 20 22:48:17 EDT 2025 Mon Oct 20 16:56:00 EDT 2025 Thu Oct 16 15:45:52 EDT 2025 Mon Jul 21 05:50:51 EDT 2025 Wed Oct 01 04:37:18 EDT 2025 Thu Apr 24 23:10:22 EDT 2025 Sat Sep 06 07:24:25 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Multi-omics integration Imaging Generative model |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c642t-322d57820a06d0efe7a38db136c1eaae3d720692a7af7c6551517595557e46093 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/s13040-024-00391-z |
| PMID | 39358793 |
| PQID | 3115132260 |
| PQPubID | 55347 |
| PageCount | 29 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3d52f810c1654bd5b733175b213b9f65 unpaywall_primary_10_1186_s13040_024_00391_z pubmedcentral_primary_oai_pubmedcentral_nih_gov_11446004 proquest_miscellaneous_3112528453 proquest_journals_3115132260 gale_infotracmisc_A811020451 gale_infotracacademiconefile_A811020451 gale_incontextgauss_ISR_A811020451 pubmed_primary_39358793 crossref_primary_10_1186_s13040_024_00391_z crossref_citationtrail_10_1186_s13040_024_00391_z springer_journals_10_1186_s13040_024_00391_z |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-10-02 |
| PublicationDateYYYYMMDD | 2024-10-02 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-10-02 day: 02 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BioData mining |
| PublicationTitleAbbrev | BioData Mining |
| PublicationTitleAlternate | BioData Min |
| PublicationYear | 2024 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | I Subramanian (391_CR11) 2020; 14 R Nativio (391_CR2) 2020; 52 ZJ Cao (391_CR42) 2022; 40 OB Bakker (391_CR1) 2018; 19 391_CR99 B Gong (391_CR5) 2021; 22 391_CR10 391_CR98 391_CR97 391_CR96 391_CR19 391_CR18 391_CR17 391_CR16 BH Menze (391_CR92) 2015; 34 391_CR26 391_CR25 391_CR24 391_CR23 391_CR22 391_CR21 391_CR20 391_CR29 S Xiao (391_CR89) 2016; 25 391_CR28 391_CR27 391_CR40 J Mariette (391_CR9) 2018; 34 R Satija (391_CR4) 2015; 33 391_CR37 M Kang (391_CR14) 2022; 23 391_CR36 391_CR35 391_CR34 391_CR33 391_CR32 391_CR31 391_CR30 JS Wekesa (391_CR13) 2023; 14 391_CR39 391_CR38 KLIII Pearson (391_CR6) 1901; 2 391_CR51 391_CR47 391_CR46 391_CR45 391_CR44 391_CR43 391_CR41 CG Peterfy (391_CR91) 2008; 16 391_CR49 KA Ellis (391_CR90) 2009; 21 391_CR62 391_CR61 391_CR60 DW Zijlmans (391_CR3) 2022; 24 V Agarwal (391_CR83) 2015; 4 391_CR59 391_CR58 391_CR57 391_CR56 391_CR55 391_CR54 391_CR53 391_CR52 391_CR101 391_CR100 391_CR73 391_CR72 391_CR71 391_CR70 391_CR69 391_CR68 391_CR67 391_CR66 391_CR65 391_CR64 391_CR63 Y Wen (391_CR15) 2023; 5 391_CR102 S Bakr (391_CR86) 2018; 5 C Lee (391_CR48) 2021; 130 J Barretina (391_CR82) 2012; 483 N Vahabi (391_CR12) 2022; 13 391_CR84 391_CR81 391_CR80 391_CR79 391_CR78 391_CR77 391_CR76 391_CR75 KT Ahmed (391_CR50) 2022; 38 W Zhang (391_CR74) 2015; 16 391_CR95 391_CR94 391_CR93 391_CR7 391_CR88 391_CR8 391_CR87 391_CR85 |
| References_xml | – ident: 391_CR30 doi: 10.1177/15353702211065010 – volume: 19 start-page: 776 issue: 7 year: 2018 ident: 391_CR1 publication-title: Nat Immunol. doi: 10.1038/s41590-018-0121-3 – volume: 14 start-page: 117793221989905 year: 2020 ident: 391_CR11 publication-title: Bioinforma Biol Insights. doi: 10.1177/1177932219899051 – volume: 13 start-page: 854752 year: 2022 ident: 391_CR12 publication-title: Front Genet. doi: 10.3389/fgene.2022.854752 – ident: 391_CR95 – ident: 391_CR36 doi: 10.1093/bib/bbab600 – ident: 391_CR26 doi: 10.1186/s13040-020-00222-x – volume: 130 start-page: 1513 year: 2021 ident: 391_CR48 publication-title: Proc Mach Learn Res. – ident: 391_CR60 doi: 10.3389/fgene.2019.00617 – ident: 391_CR40 doi: 10.1093/bioinformatics/btab403 – ident: 391_CR57 doi: 10.1016/j.media.2021.102266 – ident: 391_CR49 doi: 10.1093/bioinformatics/btab109 – ident: 391_CR75 doi: 10.1093/nar/gkz757 – volume: 16 start-page: 1433 issue: 12 year: 2008 ident: 391_CR91 publication-title: Osteoarthr Cartil / OARS Osteoarthr Res Soc. doi: 10.1016/j.joca.2008.06.016 – ident: 391_CR73 doi: 10.1038/ng.2529 – ident: 391_CR69 doi: 10.1093/nar/30.1.42 – volume: 23 start-page: bbab454 issue: 1 year: 2022 ident: 391_CR14 publication-title: Brief Bioinforma. doi: 10.1093/bib/bbab454 – ident: 391_CR65 doi: 10.1038/nm.3954 – volume: 24 start-page: 858 issue: 6 year: 2022 ident: 391_CR3 publication-title: Nat Cell Biol. doi: 10.1038/s41556-022-00932-w – ident: 391_CR98 – volume: 16 start-page: 133 issue: 1 year: 2015 ident: 391_CR74 publication-title: Genome Biol. doi: 10.1186/s13059-015-0694-1 – volume: 33 start-page: 495 issue: 5 year: 2015 ident: 391_CR4 publication-title: Nat Biotechnol. doi: 10.1038/nbt.3192 – ident: 391_CR63 doi: 10.1371/journal.pcbi.1006376 – ident: 391_CR38 doi: 10.1186/s12864-019-6285-x – ident: 391_CR64 doi: 10.1016/j.cell.2016.06.017 – ident: 391_CR19 doi: 10.1109/TCBB.2019.2909905 – ident: 391_CR22 doi: 10.1007/978-3-030-20242-2_10 – volume: 5 start-page: 180202 issue: 1 year: 2018 ident: 391_CR86 publication-title: Sci Data. doi: 10.1038/sdata.2018.202 – ident: 391_CR53 doi: 10.3390/jpm12040601 – ident: 391_CR7 – ident: 391_CR43 doi: 10.1016/j.ymeth.2019.03.004 – ident: 391_CR97 – ident: 391_CR44 doi: 10.1016/j.csbj.2021.08.006 – ident: 391_CR100 doi: 10.1038/s41746-022-00742-2 – ident: 391_CR102 doi: 10.1200/CCI.19.00126 – volume: 40 start-page: 1458 year: 2022 ident: 391_CR42 publication-title: Nat Biotechnol. doi: 10.1038/s41587-022-01284-4 – ident: 391_CR66 doi: 10.1093/bioinformatics/btw344 – ident: 391_CR78 doi: 10.1016/j.cell.2020.09.056 – volume: 21 start-page: 672 issue: 4 year: 2009 ident: 391_CR90 publication-title: Int Psychogeriatr. doi: 10.1017/S1041610209009405 – volume: 25 start-page: 131 issue: 2 year: 2016 ident: 391_CR89 publication-title: J Ment Health (Abingdon, England). doi: 10.3109/09638237.2015.1124385 – ident: 391_CR16 doi: 10.1101/531327 – ident: 391_CR84 – ident: 391_CR70 doi: 10.1093/nar/gki072 – ident: 391_CR8 – ident: 391_CR24 doi: 10.3389/fgene.2022.806842 – ident: 391_CR72 doi: 10.1093/nar/gky1131 – ident: 391_CR61 doi: 10.1016/j.media.2022.102643 – ident: 391_CR29 doi: 10.3389/fgene.2018.00477 – volume: 38 start-page: 179 year: 2022 ident: 391_CR50 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btab608 – ident: 391_CR55 doi: 10.1109/TMI.2020.3021387 – ident: 391_CR31 doi: 10.1186/s13040-023-00349-7 – ident: 391_CR28 doi: 10.1101/114892 – ident: 391_CR94 doi: 10.1101/2022.09.05.506572 – ident: 391_CR99 – ident: 391_CR47 doi: 10.3390/cancers13123047 – ident: 391_CR76 – volume: 34 start-page: 1009 issue: 6 year: 2018 ident: 391_CR9 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btx682 – ident: 391_CR67 doi: 10.1038/ng.2764 – ident: 391_CR25 doi: 10.1109/JBHI.2021.3102186 – volume: 52 start-page: 1024 issue: 10 year: 2020 ident: 391_CR2 publication-title: Nat Genet. doi: 10.1038/s41588-020-0696-0 – ident: 391_CR79 doi: 10.1038/s41467-021-22368-w – ident: 391_CR71 doi: 10.1038/s41586-019-1186-3 – ident: 391_CR27 doi: 10.1101/2021.03.02.433454 – ident: 391_CR81 doi: 10.1038/sdata.2016.15 – ident: 391_CR59 doi: 10.1109/CSCI51800.2020.00144 – volume: 4 start-page: e05005 year: 2015 ident: 391_CR83 publication-title: eLife. doi: 10.7554/eLife.05005 – volume: 34 start-page: 1993 issue: 10 year: 2015 ident: 391_CR92 publication-title: IEEE Trans Med Imaging. doi: 10.1109/TMI.2014.2377694 – ident: 391_CR80 doi: 10.1038/s41586-021-03500-8 – ident: 391_CR77 doi: 10.1038/s41587-019-0290-0 – volume: 2 start-page: 559 issue: 11 year: 1901 ident: 391_CR6 publication-title: Lond Edinb Dublin Phil Mag J Sci. doi: 10.1080/14786440109462720 – ident: 391_CR10 doi: 10.1038/s41467-021-23774-w – ident: 391_CR54 doi: 10.1155/2022/5131170 – ident: 391_CR33 doi: 10.1016/j.ymeth.2020.07.008 – ident: 391_CR34 doi: 10.1186/s12911-020-01225-8 – ident: 391_CR87 – ident: 391_CR21 doi: 10.3389/fgene.2019.00166 – ident: 391_CR52 doi: 10.1101/2023.04.30.538439 – ident: 391_CR56 – ident: 391_CR51 – ident: 391_CR45 doi: 10.1109/BIBM47256.2019.8983228 – volume: 5 start-page: 2200247 issue: 5 year: 2023 ident: 391_CR15 publication-title: Adv Intell Syst. doi: 10.1002/aisy.202200247 – ident: 391_CR62 doi: 10.1016/j.eswa.2023.120761 – ident: 391_CR88 – ident: 391_CR18 doi: 10.1101/2021.01.25.427845 – ident: 391_CR41 doi: 10.1101/2022.03.16.484643 – ident: 391_CR101 doi: 10.3389/frai.2023.1098308 – ident: 391_CR20 doi: 10.1093/bioinformatics/btac080 – ident: 391_CR93 doi: 10.1101/2019.12.13.19014902 – ident: 391_CR17 – volume: 483 start-page: 603 issue: 7391 year: 2012 ident: 391_CR82 publication-title: Nature. doi: 10.1038/nature11003 – ident: 391_CR35 doi: 10.1016/j.isci.2023.107378 – ident: 391_CR23 doi: 10.1016/j.csbj.2021.04.067 – ident: 391_CR58 doi: 10.1109/TMI.2018.2868977 – ident: 391_CR96 – ident: 391_CR32 doi: 10.3389/fgene.2020.564792 – volume: 22 start-page: 1 year: 2021 ident: 391_CR5 publication-title: Genome Biol. doi: 10.1186/s13059-021-02556-z – ident: 391_CR37 doi: 10.3389/fgene.2022.855629 – ident: 391_CR46 doi: 10.1038/s41598-021-85285-4 – ident: 391_CR85 – ident: 391_CR39 doi: 10.1038/s41598-020-70229-1 – ident: 391_CR68 – volume: 14 start-page: 1199087 year: 2023 ident: 391_CR13 publication-title: Front Genet. doi: 10.3389/fgene.2023.1199087 |
| SSID | ssj0062053 |
| Score | 2.5379846 |
| SecondaryResourceType | review_article |
| Snippet | Background
The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in... The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and... Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in... BackgroundThe rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in... Abstract Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 38 |
| SubjectTerms | Algorithms Artificial neural networks Bioinformatics Biological analysis Biomedical and Life Sciences Biomedical data Clustering Computational Biology/Bioinformatics Computer Appl. in Life Sciences Correlation analysis Data analysis Data integration Data Mining and Knowledge Discovery Datasets Deep learning Disease Generative model Genomics Graph neural networks Imaging Integration Integrative Analysis of Multi-Omics Data for Precision Medicine Knowledge representation Life Sciences Machine learning Medical imaging Medical prognosis Metabolomics Multi-omics integration Neural networks Performance enhancement Principal components analysis Probability distribution Proteomics Radiomics Review Survival analysis Task complexity Transcriptomics Variational methods |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Ni9UwEB9kQdSD-G11lSqCBzdskzZpe1w_llXUg7qwt5C0ySo8-h72PWT3r3cmTeurwurBazMtzcwvk5l28huAZ4Xw3KnSMWN5wQqT18wUMmO8MMqa1tYi9CH78FEdHRfvTuTJVqsvqgkb6IEHxe3nrRS-4llDx25sKy01GSylFTy3tVeBvTSr6jGZGnywEoit8YhMpfZ79NRUxigKFijR2flsGwps_X_65K1N6feCyemv6TW4sulW5uyHWSy2NqbDG3A9RpTpwTCTm3DJdbfg8tBj8uw2vH_t3CqNzSFOGe1abToyibs-xaA1DVWFjM4n9ymVjKYjiQQaLTUdykfqkjtwfPjmy6sjFlsosAYTizXD5doSYX1mMtVmzrvS5FVrea4a7oxxeVuKTNXClMaXjcLwSaJyayll6QqV1fld2OmWnbsPqUcbVDz3GE9gUma4xUTFS6vo-XjRJcBHjeom8otTm4uFDnlGpfRgBY1W0MEK-jyBF9M9q4Fd40Lpl2SoSZKYscMFxIuOeNF_w0sCT8nMmrgvOiquOTWbvtdvP3_SBxXGQkTPzxN4HoX8EufQmHhWATVBdFkzyd2ZJC7OZj48oklH59BrIjiijwAqS-DJNEx3UsFb55abICMkhg4yT-DeAL5p3oG0Dv1qAtUMljPFzEe6b18DdTin9B_9YgJ7I4J_vddFmt-bUP4PhnrwPwz1EK4KWrBUrCF2YWf9feMeYQC4to_DWv8J8bJP_Q priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9QwDLfGTQh4QHyvMFBBSDywaE3apO0DQhtsGghOaDBpb1HSpsekU-9Y74S2vx4713YrSCdeE6dqbMexW_tngNeJqLhTqWPG8oQlJs6ZSWTEeGKUNaXNhe9D9nWsjk6Sz6fydAPGXS0MpVV2NtEb6nJW0DfyXUKFochJRe_nvxh1jaK_q10LDdO2VijfeYixG7ApCBlrBJv7B-Nvx51tVgJ1riudydRugxac0htFwjxUOrscXE8exf9fW33tsvo7kbL_m3oHbi3rubn4babTaxfW4T2423qa4d5KNe7DhqsfwM1V78mLh_Dlo3PzsG0aMWF0m5VhhzDumhCd2dBnGzKqW25CSiUNO3AJFGZoaqRvIU0ewcnhwY8PR6xtrcAKDDgWDBlZEpB9ZCJVRq5yqYmz0vJYFdwZ4-IyFZHKhUlNlRYKeSrRz8illKlLVJTHj2FUz2q3BWHFoyLjcYV-BgZrhlsMYCppFT0fB10AvOOoLlrccWp_MdU-_siUXklBoxS0l4K-DOBtv2a-Qt1YS71PguopCTHbD8zOJ7o9gDoupagyfFcq37KltNSsMpVW8NjmlZIBvCIxa8LEqCnpZmKWTaM_fT_Wexn6SATbzwN40xJVM9xDYdoaBuQEwWgNKLcHlHhoi-F0p026NRqNvlLxAF7207SSEuFqN1t6GiHRpZBxAE9Wytfv24PZob0NIBuo5YAxw5n67KeHFOf0WQDtZQA7nQZfvdc6zu_0Wv4fgnq6ftfP4Lago0jpGWIbRovzpXuOLt_CvmjP8R_grk7w priority: 102 providerName: ProQuest – databaseName: Springer Nature Link dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5BEaIcEM-SUlBASByoRezETnIshaog4ABU6s2yE6ettMquyK5Q--s74zhhA6iCazyOEs9bnvkG4GUmGu5U7pixPGOZSUtmMpkwnhllTW1L4eeQff6iDo-yj8fyOMDkUC_M-v09L9SbDm0sFSCKjHkwc3ZxHW6gk1L-YlbtD1ZXCZSmoSnmr_smjsfj8_9phdfc0O8lkuM96W24tWoX5vynmc3WXNHBXbgTYsh4r2f6Pbjm2vtws58qef4APr1zbhGHcRAnjPxUHQ_Y4a6LMUyNfR0ho47kLqYi0XiAjUA2xaZF-gBW8hCODt5_3z9kYWgCqzCVWDJU0Jog6hOTqDpxjctNWtSWp6rizhiX1rlIVClMbpq8UhgwSYwgSill7jKVlOkj2GjnrXsMccOTquBpgxEEpmGGW0xNGmkVvR8fugj4cKK6CojiNNhipn1mUSjdc0EjF7Tngr6I4PW4Z9HjaVxJ_ZYYNVISFrZ_gCKig2rptJaiKfBbqTHL1tLSGMpcWsFTWzZKRvCC2KwJ7aKlcpoTs-o6_eHbV71XYPRDgPw8gleBqJnjP1QmdCfgSRBA1oRyZ0KJ6lhNlwdp0sEcdJogjSjtV0kEz8dl2kklbq2brzyNkBgsyDSCrV74xv_2MHVoSSMoJmI5OZjpSnt26sHCOSX8aAkj2B0k-Nd3XXXyu6OU_wOjtv_v7U9gU5BqUiGG2IGN5Y-Ve4rB3dI-81p9CW3PQdQ priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9QwEB7BVgh44D4CBQWExAN1iZPYSR6XoyoIKgSs1D5ZduIUxCq7Ihuh7q9nxjnYFFSV13gcxXN57Mx8A_AsDktuZWKZNjxmsY4ypmMRMB5raXRhstD1Ift4IPdn8ftDcdjB5FAtzOb_e57KlzX6WEpADGPmwMzZ-iJsSYFx9wS2Zgefpkeu4lHgsRj3nr4q5p8TRzuPA-j_2w1v7EOncySHH6VX4XJTLfXJLz2fb-xFe9fbpka1gzCkFJQfu83K7ObrUwCP51vmDbjWhaT-tNWhm3DBVrfgUtuk8uQ2fHhj7dLvukscM9r2Cr-HIre1j1Gv79ISGRU41z7lnPo9CgVK3dcV0nfYJ3dgtvf26-t91vVgYDmeTFYM7b0gxPtAB7IIbGkTHaWF4ZHMudXaRkUSBjILdaLLJJcYfwmURiaESGwsgyy6C5NqUdn74Jc8yFMelRiQ4KlOc4MnnVIYSe_Hh9YD3stH5R1AOfXJmCt3UEmlarmkkEvKcUmtPXgxzFm28BxnUr8isQ-UBK3tHqAoVGepKipEWKb4rVTnZQphqKtlIkzII5OVUnjwlJRGEXhGRdk5x7qpa_Xuy2c1TTGYInx_7sHzjqhc4Bpy3RU7ICcIb2tEuT2iROvOx8O9bqrOu9SKEJLoFkEGHjwZhmkmZcxVdtE4mlBg7CEiD-61qjys26HeoWP2IB0p-Ygx45Hq-zeHPc7p_gAdqwc7vT38-a6zOL8z2Mw5BPXg_8gfwpWQTIfyOsJtmKx-NvYRxoor87hzEr8BN5JafQ priority: 102 providerName: Unpaywall |
| Title | Deep learning-based approaches for multi-omics data integration and analysis |
| URI | https://link.springer.com/article/10.1186/s13040-024-00391-z https://www.ncbi.nlm.nih.gov/pubmed/39358793 https://www.proquest.com/docview/3115132260 https://www.proquest.com/docview/3112528453 https://pubmed.ncbi.nlm.nih.gov/PMC11446004 https://doi.org/10.1186/s13040-024-00391-z https://doaj.org/article/3d52f810c1654bd5b733175b213b9f65 |
| UnpaywallVersion | publishedVersion |
| Volume | 17 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central Open Access Free customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: RBZ dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: KQ8 dateStart: 20080101 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: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: DOA dateStart: 20080101 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: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: ABDBF dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: ADMLS dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: DIK dateStart: 20080101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: GX1 dateStart: 20080101 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: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: M~E dateStart: 20080101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: RPM dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 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: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: 8FG dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: 8C1 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1756-0381 dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: M48 dateStart: 20081101 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: HAS SpringerNature Open Access 2022 customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: AAJSJ dateStart: 20081201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature Link customDbUrl: eissn: 1756-0381 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0062053 issn: 1756-0381 databaseCode: C6C dateStart: 20080112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED_tQwh4QHwTGFVASDywQJzETvKAUFdWRsWqaaNSebKcxClIVVqaVtD99dy5SVhgmhAvqWSfK-c-7Dvn_DuAF4GXMy1C7aiEBU6g_NhRAXcdFiiRqCyJPVOH7HgojkbBYMzHW1CXO6oYWF4a2lE9qdFi-vrn9_U7NPi3xuAj8abEdZiSFL3AMYDnzvk27OJOFVMph-Og-aogPNS4-uLMpeNam5PB8P97pb6wVf2ZRtl8S70J11fFXK1_qOn0wnbVvw23Kj_T7m4U4w5s6eIuXNtUnlzfg0_vtZ7bVcmIiUN7WWbX-OK6tNGVtU2uoUO3lkubEkntGloCRWmrAukrQJP7MOoffu4dOVVhBSfFcGPpoBFnBGPvKldkrs51qPwoS5gvUqaV0n4Weq6IPRWqPEwFOlUcvYyYcx7qQLix_wB2ilmhH4GdMzeNmJ-jl4GhmmIJhi85TwT9PzZqC1jNUZlWqONU_GIqTfQRCbmRgkQpSCMFeW7Bq2bMfIO5cSX1AQmqoSS8bNMwW0xkZX7Sz7iXRzhXuryVZDyhUpUhTzzmJ3EuuAXPScySEDEKSrmZqFVZyo9np7IboYdEoP3MgpcVUT7Dd0hVdYMBOUEgWi3KvRYlmmza7q61SdYaLwn2iI4GhGvBs6abRlIaXKFnK0PjcXQouG_Bw43yNe9toOxwtbUgaqllizHtnuLbVwMozuhQAFdLC_ZrDf49r6s4v99o-T8I6vH_T-wJ3PDITClxw9uDneVipZ-iM7hMOrAdjkN8Rj1Gz_6HDux2u4OzAf4eHA5PTrG1J3odc9jSMesB9oyGJ90vvwCUD2D1 |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB5VRahwQLwxFDAIxIGu6l17184BoUKpEvo4QCvltl3b64AUOaFOVKU_it_IzMZ2a5AiLr16x5Z3ZnYe9sw3AG8iUXCrYstMyiMWmbDHTCQDxiOjUpOnPeHmkB0eqf5J9HUoh2vwu-mFobLKxiY6Q51PMvpGvk2oMJQ5qeDj9BejqVH0d7UZobFUi327OMeUrfow2EX5vhVi78vx5z6rpwqwDGPtGcNn5IThHphA5YEtbGzCJE95qDJujbFhHotA9YSJTRFnCiMKiS62J6WMbaQc-BKa_BtRiLYEz088bBM8JVCjm8acRG1X6B-oeFJEzAGxs4uO83MzAv71BFdc4d9lmu2_2tuwMS-nZnFuxuMr7nDvLtyp41h_Z6l492DNlvfh5nKy5eIBHOxaO_XrkRQjRr4y9xv8clv5GCr7rpaRUVd05VOhqt9AV6Cq-KZE-how5SGcXAuLH8F6OSntE_ALHmQJDwuMYjAVNDzF9KiQqaLn40XrAW84qrMa1ZyGa4y1y24SpZdS0CgF7aSgLzx4394zXWJ6rKT-RIJqKQmP212YnI10fbx1mEtRJPiu1ByW5jKlUZixTAUP016hpAevScyaEDdKKukZmXlV6cH3b3onwQiMhgJwD97VRMUE95CZukMCOUEgXR3KzQ4lmoSsu9xok65NUqUvD5AHr9plupPK7Eo7mTsaITFgkaEHj5fK1-7bQeWhNfcg6ahlhzHdlfLnDwdYzumjA1pjD7YaDb58r1Wc32q1_D8E9XT1rl_CRv_48EAfDI72n8EtQceSCkHEJqzPzub2OQaXs_SFO9E-nF63CfkDTGKDgQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5BEa8D4k2gQEBIHKjV2Imd5Fi2VC2UCgGVerPsxFmQVtlVsyvU_npmnAcbQBVc47HleJ5OZr4BeJWIijuVOmYsT1hi4pyZREaMJ0ZZU9pc-D5kH4_U_nHy_kSerFXx-2z3_pdkW9NAKE31cntRVq2KZ2q7QctLaYkiYR7inJ1fhisJejfqYTBRk94WK4Ey1pfK_HXeyB151P4_bfOac_o9cXL4e3oTrq_qhTn7YWazNQe1dxtudZFluNOKwh245Oq7cLXtNXl2Dw53nVuEXZOIKSPvVYY9orhrQgxeQ59dyKhOuQkpdTTswSSQeaGpkb6DMLkPx3vvvk72WddKgRV4wVgyVNuSgOsjE6kycpVLTZyVlseq4M4YF5epiFQuTGqqtFAYRkmMK3IpZeoSFeXxA9io57V7BGHFoyLjcYVxBV7ODLd4YamkVbQ-PnQB8P5EddHhjFO7i5n2941M6ZYLGrmgPRf0eQBvhjmLFmXjQuq3xKiBkhCy_YP56VR3CqfjUooqw71SuZYtpaXmlKm0gsc2r5QM4CWxWRMGRk1JNlOzahp98OWz3skwJiKYfh7A646omuM7FKarWcCTINisEeXmiBKVtBgP99KkOyPRaAI6oo8BKgrgxTBMMynxrXbzlacREkMIGQfwsBW-4b09eB3a1wCykViODmY8Un__5iHEOX0GQPsYwFYvwb_2ddHJbw1S_g-Mevx_qz-Ha5929_ThwdGHJ3BDkJZSpobYhI3l6co9xehvaZ95Bf8JSehNCg |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9QwEB7BVgh44D4CBQWExAN1iZPYSR6XoyoIKgSs1D5ZduIUxCq7Ihuh7q9nxjnYFFSV13gcxXN57Mx8A_AsDktuZWKZNjxmsY4ypmMRMB5raXRhstD1Ift4IPdn8ftDcdjB5FAtzOb_e57KlzX6WEpADGPmwMzZ-iJsSYFx9wS2Zgefpkeu4lHgsRj3nr4q5p8TRzuPA-j_2w1v7EOncySHH6VX4XJTLfXJLz2fb-xFe9fbpka1gzCkFJQfu83K7ObrUwCP51vmDbjWhaT-tNWhm3DBVrfgUtuk8uQ2fHhj7dLvukscM9r2Cr-HIre1j1Gv79ISGRU41z7lnPo9CgVK3dcV0nfYJ3dgtvf26-t91vVgYDmeTFYM7b0gxPtAB7IIbGkTHaWF4ZHMudXaRkUSBjILdaLLJJcYfwmURiaESGwsgyy6C5NqUdn74Jc8yFMelRiQ4KlOc4MnnVIYSe_Hh9YD3stH5R1AOfXJmCt3UEmlarmkkEvKcUmtPXgxzFm28BxnUr8isQ-UBK3tHqAoVGepKipEWKb4rVTnZQphqKtlIkzII5OVUnjwlJRGEXhGRdk5x7qpa_Xuy2c1TTGYInx_7sHzjqhc4Bpy3RU7ICcIb2tEuT2iROvOx8O9bqrOu9SKEJLoFkEGHjwZhmkmZcxVdtE4mlBg7CEiD-61qjys26HeoWP2IB0p-Ygx45Hq-zeHPc7p_gAdqwc7vT38-a6zOL8z2Mw5BPXg_8gfwpWQTIfyOsJtmKx-NvYRxoor87hzEr8BN5JafQ |
| 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=Deep+learning-based+approaches+for+multi-omics+data+integration+and+analysis&rft.jtitle=BioData+mining&rft.au=Ballard%2C+Jenna%C2%A0L.&rft.au=Wang%2C+Zexuan&rft.au=Li%2C+Wenrui&rft.au=Shen%2C+Li&rft.date=2024-10-02&rft.pub=BioMed+Central&rft.eissn=1756-0381&rft.volume=17&rft_id=info:doi/10.1186%2Fs13040-024-00391-z&rft.externalDocID=PMC11446004 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1756-0381&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1756-0381&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1756-0381&client=summon |