This comprehensive review evaluates 32 distinct methods for integrative pathway analysis using multi-omics and multi-cohort data. The authors categorize these tools into four strategic groups: P-value-based gene-level, P-value-based pathway-level, graph-transformation, and machine-learning approaches. By examining methodological assumptions, software availability, and computational performance, the survey assists researchers in selecting the most appropriate tools for complex biological datasets. The text highlights how combining diverse data types—such as transcriptomics, proteomics, and metabolomics—increases statistical power and provides a more holistic view of disease phenotypes. Ultimately, the source identifies current technological challenges, such as the need for better data integration and interactive visualization in bioinformatics.

References:

• Maghsoudi Z, Nguyen H, Tavakkoli A, et al. A comprehensive survey of the approaches for pathway analysis using multi-omics data integration[J]. Briefings in Bioinformatics, 2022, 23(6): bbac435.