Integrative Computational Approaches for Omics Data Analysis and Disease Biomarker Discovery

The rapid accumulation of omics data—spanning genomics, transcriptomics, proteomics, and beyond—has transformed the landscape of disease research. However, translating these extensive datasets into meaningful biological and clinical insights remains a major challenge. Advances in artificial intelligence (AI), machine learning, and computational biology are beginning to address this bottleneck, equipping researchers with powerful tools to integrate diverse data layers and unravel complex disease mechanisms. These cutting-edge methodologies promise not only to accelerate biomarker discovery but also to refine therapeutic target identification, ultimately bridging the gap between big data and actionable outcomes.This Research Topic aims to spotlight innovative methodologies and practical applications at the intersection of bioinformatics, computational biology, and disease research. We invite contributions that showcase how AI-driven and computational approaches are pushing the boundaries of biomarker discovery, disease model prediction, and therapeutic development. Key goals include illustrating advances in multi-omics data integration, evaluating new bioinformatics tools and resources, and presenting workflows that connect computational predictions with experimental or clinical validation. By aggregating diverse perspectives—ranging from methodological frameworks to real-world case studies—we hope to foster interdisciplinary dialogue and catalyze future directions in computational disease research.We welcome manuscripts relevant to the following themes:- Novel machine learning strategies for multi-omics data integration,- Development and optimization of computational pipelines for biomarker and target gene identification,- Creation and application of bioinformatics resources for data mining, network analysis, and visualization,- Translational bioinformatics approaches connecting computational discoveries to laboratory or clinical outcomes,- Case studies demonstrating the use of computational tools in disease model prediction or personalized medicine. Submissions focusing on methodological innovation, practical implementation, and translational impact are particularly encouraged. Authors are invited to discuss both the opportunities and challenges in leveraging computational tools to unlock the full potential of omics data in disease research. Topic editors: Andaç Batur Çolak, Shahnawaz Ali, Dr. Hamid Qureshi Source: https://www.frontiersin.org/research-topics/73426/integrative-computational-approaches-for-omics-data-analysis-and-disease-biomarker-discovery