| Description |
1 online resource (vi, 168 pages) : illustrations (chiefly color) |
| Contents |
Intro -- Contents -- Introduction to Multiomics Technology -- 1 Genomics -- 2 Transcriptomics -- 3 Proteomics -- 4 Foodomics -- 5 Metabolomics -- 6 Epigenomics -- 7 Summary -- References -- Machine Learning from Multi-omics: Applications and DataIntegration -- 1 Introduction -- 2 Multi-omics as Cancer Indicators -- 3 Multi-omics Epigenetic Alterations of Alzheimer's -- 4 Multi-omics Applications in Mental Health and Psychiatric Disorders -- 5 Cardiovascular Disease -- 6 Machine Learning Applications to Multi-omics Data -- 7 Data Integration Strategies -- References |
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Machine Learning Approaches for Multi-omics Data Integration in Medicine -- 1 Introduction -- 2 Main Objectives of Multi-omic Data Integration Studies -- 2.1 Diagnosis and Prognosis -- 2.2 Identification of the Subtype -- 2.3 Discovering Molecular Patterns of Disease -- 2.4 Predicting the Effects of a Drug at the Molecular Level -- 2.5 Comprehension of the Regulatory Processes -- 3 Multi-omics Integration Strategies -- 3.1 Early Integration -- 3.2 Mixed Integration -- 3.3 Intermediate Integration -- 3.4 Late Integration -- 3.5 Hierarchical Integration |
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4 Machine Learning Approaches Used in Multiomics Integration -- 4.1 Data Integration Analysis for Biomarker Discovery Using Latent Components (DIABLO) -- 4.2 Multi-omics Factor Analysis (MOFA) -- 4.3 Sparse Canonical Correlation Analysis (sCCA) -- 4.4 Multi-omics Late Integration (MOLI) -- 4.5 Cancer Drug Response Prediction Using a Recommender System (CaDRReS) -- 4.6 Heterogeneous Network-Based Method for Drug Response Prediction (HNMDRP) -- 4.7 Multiple Pairwise Kernels for Drug Bioactivity Prediction (pairwiseMKL) -- 4.8 iCluster, iClusterPlus, and iClusterBayes -- 4.9 moCluster |
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4.10 Similarity Network Fusion (SNF) -- 4.11 NEighborhood Based Multi-omics Clustering (NEMO) -- 4.12 Random Walk with Restart for Multi-dimensional Data Fusion (RWRF) and Random Walk with Restart and Neighbor Information-Based Multi-dimensional Data Fusion (RWRNF) -- 5 Conclusion -- References -- Multimodal Methods for Knowledge Discovery from Bulk and Single-Cell Multi-Omics Data -- 1 Introduction -- 2 Description of Various Omics Datasets -- 2.1 ChIP-seq -- 2.2 ATAC-seq -- 2.3 Hi-C -- 2.4 Mass Spectrometry for Proteomics -- 2.5 Single-Cell Multi-Omic Profiling |
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3 Multimodal Methods for Dimensionality Reduction and Clustering -- 3.1 Non-negative Matrix Factorization -- 3.2 Tensor Decomposition -- 3.3 Multi-View Relational Learning -- 3.4 Canonical Correlation Analysis -- 3.5 Deep Learning Methods for Multimodal Dimension Reduction and Clustering -- 3.6 Evaluating and Visualizing Single-Cell Embeddings -- 4 Multimodal Methods for Inferring Gene Regulatory Networks from Bulk and Single-Cell Omics Data -- 4.1 Multiple Regression -- 4.2 Correlation and Mutual Information -- 4.3 Ordinary Differential Equation |
| Summary |
The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. 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 many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying 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 on various multi-omics data sets |
| Notes |
Description based upon print version of record |
| Subject |
Bioinformatics -- Data processing
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Genomics -- Data processing
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Systems biology -- Data processing
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Machine learning.
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Genomics -- Data processing
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Machine learning
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| Form |
Electronic book
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| Author |
Alkhateeb, Abedalrhman
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Rueda, Luis.
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| ISBN |
9783031365027 |
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303136502X |
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