Computational Systems Biology Methods for Cross‐Disease Comparison of Omics Data

ABSTRACT Complex diseases often share genetic susceptibility factors, molecular pathways, and pathological mechanisms. Understanding these commonalities through systematic cross‐disease comparisons can reveal both disease‐specific and shared biomarkers, potentially suggesting new therapeutic targets...

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Vydáno v:Wiley interdisciplinary reviews. Computational molecular science Ročník 15; číslo 4; s. e70042 - n/a
Hlavní autoři: Svinin, Gleb, Loo, Rebecca Ting Jiin, Soudy, Mohamed, Nasta, Francesco, Le Bars, Sophie, Glaab, Enrico
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA Wiley Periodicals, Inc 01.07.2025
Wiley Subscription Services, Inc
Témata:
Artificial intelligence
bioinformatics workflows
Biology
Biomarkers
Comparative analysis
Computer applications
cross‐disease comparison
Data science
Deep learning
Disease
disease networks
Expert systems
Learning algorithms
Machine learning
Medical research
multi‐omics integration
pathway analysis
Software
Susceptibility
systems biology
Therapeutic targets
ISSN:1759-0876, 1759-0884
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Shrnutí:ABSTRACT Complex diseases often share genetic susceptibility factors, molecular pathways, and pathological mechanisms. Understanding these commonalities through systematic cross‐disease comparisons can reveal both disease‐specific and shared biomarkers, potentially suggesting new therapeutic targets and opportunities for drug repurposing. In recent years, the growth of multi‐omics datasets across diverse diseases, coupled with advances in computational systems biology, has enabled sophisticated cross‐disease analyses. New methodological frameworks have emerged for integrating and comparing disease‐specific molecular signatures, from gene‐level analyses to complex network‐based approaches. Here, we present a comprehensive framework for computational cross‐disease comparison and integration of omics data, covering established and emerging methodologies. These include gene‐level comparative analyses, pathway‐based approaches, network biology methods, matrix factorization techniques, and machine learning approaches. We examine important aspects of data preprocessing, normalization, and integration, suggesting practical solutions to common technical challenges. We provide a detailed overview of relevant software tools and databases, discussing their strengths, limitations, and optimal use cases for cross‐disease analysis. Finally, we explore current trends in cross‐disease omics analysis, particularly through deep learning methods, highlighting new opportunities for methodological innovation and biological discovery in this field. This compilation of computational methods and practical insights aims to serve as a resource both for bioinformaticians seeking guidance on optimal method selection and biomedical researchers interested in applied cross‐disease analyses. In addition to highlighting practical recommendations and common pitfalls, it provides an entry point to the extensive literature in the field, supporting readers in identifying and further exploring suitable methods for their research needs. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Databases and Expert Systems Streamlined overview of the proposed comprehensive workflow for computational cross‐disease omics analysis showing the progression from data acquisition through analysis to interpretation. The workflow begins with data sources collection and preprocessing, followed by two complementary methodological tiers: Scope‐Specific Analysis Methods (Gene‐level, Pathway‐level, and Network‐level Analysis) and Generic Computational Methodologies (Matrix Factorization and Machine Learning) that converge into an integrated interpretation of findings. Arrows indicate that generic methodologies can be used within preprocessing and scope‐specific analyses, as well as on their own. Finally, extensions to emerging omics technologies and federated learning environments are covered.
Bibliografie:Gleb Svinin, Rebecca Ting Jiin Loo, Francesco Nasta, Mohamed Soudy, and Sophie Le Bars have contributed equally to the work and share the first authorship.
This work was supported by the Luxembourg National Research Fund (FNR) as part of the projects AsynIntact (C24/BM/18865990/AsynIntact), RECAST (INTER/22/17104370/RECAST), EPI_T‐ALL (INTER/TRANSCAN23/18333087/EPI_T‐ALL), AD‐PLCG2 (INTER/JPND23/17999421/AD‐PLCG2) and PreDYT (INTER/JPND23/17999421/AD‐PLCG2).
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ISSN:1759-0876
1759-0884
DOI:10.1002/wcms.70042