Computational Molecular Biology 2024, Vol.14, No.2, 84-94 http://bioscipublisher.com/index.php/cmb 90 implicated in disease progression, providing potential targets for therapeutic intervention (Tuncbag et al., 2016). In another study, multi-omics integration was applied to classify subtypes of cancer, revealing that patients with similar molecular profiles had distinct survival outcomes. By integrating gene expression, DNA methylation, and mutation data, researchers were able to identify new cancer subtypes that were previously indistinguishable using single-omics approaches (Nguyen et al., 2017) (Figure 2). Figure 2 Summary of omics integrator (Adopted from Nguyen et al., 2017) Image caption: (A) Garnet identifies transcription factors (triangles) associated with mRNA expression changes by incorporating epigenetic changes nearby expressed genes, scanning those regions for putative transcription factor binding sites and then regressing transcription factor affinity scores against gene expression changes. The result is a set of transcription factor candidates and the relative confidence that they are responsible for the observed expression changes. (B) Forest identifies a condition-specific functional sub-network from user data and a confidence-weighted interactome. The network can be composed of protein-protein, protein-metabolite or other interactions. The set of omic hits are composed of the TFs obtained from Garnet (triangles) merged with other types of hits such as differentially expressed proteins, significantly phosphorylated proteins, metabolites, etc. (circles). (C) Finally, the confidence-weighted interactome is integrated with the ‘omic’ hits using the prize-collecting Steiner forest algorithm, where the data is either connected directly or via intermediate nodes, called ‘Steiner nodes’ (Adopted from Nguyen et al., 2017). Additionally, Mendelian randomization has been used to integrate genomics with other omics data, enabling the discovery of causal relationships between genetic variants and complex traits such as diabetes and cardiovascular disease (Correa-Aguila et al., 2022). These case studies highlight the importance of integrating multiple omics layers to better understand disease mechanisms and improve clinical decision-making. 6 Applications in Disease Network Analysis 6.1 Identifying causal genes and pathways Identifying causal genes and pathways is a fundamental challenge in understanding the molecular mechanisms underlying diseases. With the rise of high-throughput omics technologies, researchers can generate vast amounts of genomic, transcriptomic, proteomic, and metabolomic data, making it possible to explore causal relationships
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