Multi-Omic Bicluster Association Analysis (MOBAA)–A tool for identifying population subgroups with distinct multi-omics molecular profiles
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ID: 316073
2026
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Abstract
Abstract Motivation The increasing availability of multi-omic datasets from the same individuals presents the opportunity to uncover distinct molecular profiles across subgroups within a study population. These profiles may be linked to specific biological traits, such as disease status, and have distinct disease trajectories. Importantly, they could reveal robust, multi-layered molecular signatures with potential applications in early diagnosis, prognosis, and treatment advancing precision medicine. Although several integrative multi-omic methods have been developed in recent years, most are tailored to population-level analyses and are not well-suited for identifying signals specific to subpopulations. Results We developed MOBAA (Multi-Omic Bicluster Association Analysis), a novel data-driven integrative machine-learning framework for identifying subgroups within a study population that exhibit distinct multi-omic molecular profiles. MOBAA is scalable and capable of handling multiple omics simultaneously without relying on parametric distributional assumptions. It combines biclustering algorithms with hierarchical clustering-based module identification and uses permutation-derived empirical p-values. This approach provides a comprehensive and intuitive view of underlying biological variation and population heterogeneity facilitating discovery of complex, multi-layered molecular signatures. Availability and Implementation The code is available as MOBAA R package. All source code as well as comprehensive documentation and examples are provided at https://github.com/pmishra912/MOBAA.
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openalex_W7163651769
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| Authors | Binisha H. Mishra, Pashupati P Mishra |
| Journal | Bioinformatics advances |
| Year | 2026 |
| DOI |
10.1093/bioadv/vbag156
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| URL | |
| Keywords | Keywords not found |
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