Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 65,106 bioRxiv papers from 288,545 authors.
We developed iOmicsPASS, an intuitive method for network-based multi-omics data integration and detection of biological subnetworks for phenotype prediction. The method converts abundance measurements into co-expression scores of biological networks and uses a powerful phenotype prediction method adapted for network-wise analysis. Simulation studies show that the proposed data integration approach considerably improves the quality of predictions. We illustrate iOmicsPASS through the integration of quantitative multi-omics data using transcription factor regulatory network and protein-protein interaction network for cancer subtype prediction. Our analysis of breast cancer data identifies network signatures surrounding established markers of molecular subtypes. The analysis of colorectal cancer data highlights a protein interactome surrounding key proto-oncogenes as predictive features of subtypes,rendering them more biologically interpretable than the approaches integrating data without a priori relational information. However, the results indicate that current molecular subtyping is overly dependent on transcriptomic data and crude integrative analysis fails to account for molecular heterogeneity in other -omics data. The analysis also suggest that tumor subtypes are not mutually exclusive and future subtyping should therefore consider multiplicity in assignments.
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