MUMA: A Multi-Omics Meta-Learning Algorithm for Data Interpretation and Classification

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 4; pp. 2428 - 2436
• Main Authors: Huang, Hai-Hui, Shu, Jun, Liang, Yong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive sampling; Algorithms; Analytical models; Bioinformatics; Biological activity; Biological analysis; Biological information theory; Biological properties; Biological samples; Biological system modeling; Biomarkers; Cancer; Classification; Data analysis; Data integration; Data interpretation; Data models; Heterogeneity; Information processing; integration analysis; Learning; Machine learning; markers selection; meta-learning; Metalearning; Molecular modelling; Multi-omics; Proteomics; Regularization; Training; Transcriptomics
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3363081

Multi-omics data integration is a promising field combining various types of omics data, such as genomics, transcriptomics, and proteomics, to comprehensively understand the molecular mechanisms underlying life and disease. However, the inherent noise, heterogeneity, and high dimensionality of multi-omics data present challenges for existing methods to extract meaningful biological information without overfitting. This paper introduces a novel Multi-Omics Meta-learning Algorithm (MUMA) that employs self-adaptive sample weighting and interaction-based regularization for enhanced diagnostic performance and interpretability in multi-omics data analysis. Specifically, MUMA captures crucial biological processes across different omics layers by learning a flexible sample reweighting function adaptable to various noise scenarios. Additionally, MUMA incorporates an interaction-based regularization term, encouraging the model to learn from the relationships among different omics modalities. We evaluate MUMA using simulations and eighteen real datasets, demonstrating its superior performance compared to state-of-the-art methods in classifying biological samples (e.g., cancer subtypes) and selecting relevant biomarkers from noisy multi-omics data. As a powerful tool for multi-omics data integration, MUMA can assist researchers in achieving a deeper understanding of the biological systems involved.