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- Martinez, DavidLinköpings universitet,Bioinformatik,Tekniska fakulteten,Linköping University (author)
NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures
- Article/chapterEnglish2023
Publisher, publication year, extent ...
- OXFORD UNIV PRESS,2023
- electronicrdacarrier
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- LIBRIS-ID:oai:research.chalmers.se:b08e55e7-e74b-4341-a2f8-c092aea1f10c
- https://doi.org/10.1093/bib/bbad293DOI
- https://research.chalmers.se/publication/537413URI
- https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-197471URI
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- Language:English
- Summary in:English
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- Funding Agencies|Swedish Research Council [2019-04193]; Wallenberg AI, Autonomous Systems and Software Program (WASP); SciLifeLab and Wallenberg National~Program for Data-Driven Life Science (DDLS) [WASPDDLS21-040/KAW 2020.0239]
- Precision medicine relies on the identification of robust disease and risk factor signatures from omics data. However, current knowledge-driven approaches may overlook novel or unexpected phenomena due to the inherent biases in biological knowledge. In this study, we present a data-driven signature discovery workflow for DNA methylation analysis utilizing network-coherent autoencoders (NCAEs) with biologically relevant latent embeddings. First, we explored the architecture space of autoencoders trained on a large-scale pan-tissue compendium (n = 75 272) of human epigenome-wide association studies. We observed the emergence of co-localized patterns in the deep autoencoder latent space representations that corresponded to biological network modules. We determined the NCAE configuration with the strongest co-localization and centrality signals in the human protein interactome. Leveraging the NCAE embeddings, we then trained interpretable deep neural networks for risk factor (aging, smoking) and disease (systemic lupus erythematosus) prediction and classification tasks. Remarkably, our NCAE embedding-based models outperformed existing predictors, revealing novel DNA methylation signatures enriched in gene sets and pathways associated with the studied condition in each case. Our data-driven biomarker discovery workflow provides a generally applicable pipeline to capture relevant risk factor and disease information. By surpassing the limitations of knowledge-driven methods, our approach enhances the understanding of complex epigenetic processes, facilitating the development of more effective diagnostic and therapeutic strategies.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Bioinformatik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Bioinformatics hsv//eng
- NATURVETENSKAP Biologi Bioinformatik och systembiologi hsv//swe
- NATURAL SCIENCES Biological Sciences Bioinformatics and Systems Biology hsv//eng
- DNA methylation
- deep learning
- autoencoders
- biomarkers
- systems medicine
- transfer learning
Added entries (persons, corporate bodies, meetings, titles ...)
- Dwivedi, SanjivLinköpings universitet,Bioinformatik,Tekniska fakulteten,Linköping University(Swepub:liu)sandw73 (author)
- Jörnsten, Rebecka,1971Chalmers tekniska högskola,Chalmers University of Technology,Göteborgs universitet,University of Gothenburg,Chalmers Univ Technol, Sweden(Swepub:cth)jornsten (author)
- Gustafsson, MikaLinköpings universitet,Bioinformatik,Tekniska fakulteten,Linköping University(Swepub:liu)mikgu75 (author)
- Linköpings universitetBioinformatik (creator_code:org_t)
Related titles
- In:Briefings in Bioinformatics: OXFORD UNIV PRESS24:51467-54631477-4054
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- https://research.chalmers.se/publication/537413/file/537413_Fulltext.pdfFULLTEXT
- https://liu.diva-portal.org/smash/get/diva2:1794557/FULLTEXT01.pdffulltext:print
- https://doi.org/10.1093/bib/bbad293
- https://research.chalmers.se/publication/537413
- https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-197471
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