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 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