Machine Learning Methods for Multi-Omics Data Integration

The advancement of biomedical engineering technology has enabled the generation of multi-omics data by developing high-throughput technologies, including next-generation sequencing, mass spectrometry, and microarray analysis. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in various research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables to obtain a more comprehensive understanding of complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into one learning model also comes with challenges. Machine learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model.

This book comprehensively overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validations. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, transfer learning, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late integration among multi-view models. The applied models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data.

Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques in their research