NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction

• Published in: Briefings in bioinformatics Vol. 25; no. 5
• Main Authors: Zhu, Bencong, Zhang, Zhen, Leung, Suet Yi, Fan, Xiaodan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 25.07.2024; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Biological analysis; Biological effects; Biomarkers; Biomarkers - metabolism; Computational Biology - methods; Data analysis; Data augmentation; DNA methylation; Gene Regulatory Networks; Genomics - methods; Handles; Humans; Information processing; Multiomics; Predictions; Problem Solving Protocol; multi-omics integrative; Markov random field; data augmentation; block missingness
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae454

Abstract Compared with analyzing omics data from a single platform, an integrative analysis of multi-omics data provides a more comprehensive understanding of the regulatory relationships among biological features associated with complex diseases. However, most existing frameworks for integrative analysis overlook two crucial aspects of multi-omics data. Firstly, they neglect the known dependencies among biological features that exist in highly credible biological databases. Secondly, most existing integrative frameworks just simply remove the subjects without full omics data to handle block missingness, resulting in decreasing statistical power. To overcome these issues, we propose a network-based integrative Bayesian framework for biomarker selection and disease outcome prediction based on multi-omics data. Our framework utilizes Dirac spike-and-slab variable selection prior to identifying a small subset of biomarkers. The incorporation of gene pathway information improves the interpretability of feature selection. Furthermore, with the strategy in the FBM (stand for ”full Bayesian model with missingness”) model where missing omics data are augmented via a mechanistic model, our framework handles block missingness in multi-omics data via a data augmentation approach. The real application illustrates that our approach, which incorporates existing gene pathway information and includes subjects without DNA methylation data, results in more interpretable feature selection results and more accurate predictions.