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Multi-Omics Data Integration with Python: Unifying Genomics, Transcriptomics, Proteomics, and Metabolomics for Systems-Level Biological Insight
Reactive PublishingThe next frontier of biological research is integrative. As genomics, transcriptomics, proteomics, and metabolomics mature into high-throughput data sciences, the true power of modern biology lies not in siloed analysis, but in their synthesis. Multi-omic integration reveals regulatory interactions, mechanistic pathways, and phenotype-defining patterns that no single modality can capture alone.
This book offers a comprehensive guide to multi-omics analysis using Python and open scientific tooling. Readers learn how to unify heterogeneous datasets, construct multi-layer models, map biological networks, and derive systems-level insights from complex molecular profiles. Through hands-on examples and end-to-end workflows, the book demonstrates practical techniques for feature harmonization, joint dimensionality reduction, tensor-based integration, regulatory inference, deep learning architectures for multi-modal fusion, and network-based interpretation frameworks.
Inside, you will learn how
• Load and harmonize genomic, transcriptomic, proteomic, and metabolomic datasets
• Perform cross-modal normalization, alignment, and missing data resolution
• Apply graphical, statistical, and machine learning approaches for multi-omic fusion
• Use network and pathway models to interpret regulatory interactions
• Deploy deep learning models for representation learning and phenotype prediction
• Integrate multi-omic data with EHR, imaging, and clinical metadata
• Build end-to-end pipelines for biomarker discovery and mechanistic insight
Designed for computational biologists, data scientists, systems biologists, and researchers in molecular medicine, this book provides both theoretical foundations and practical workflows that translate directly to research and industry applications. By combining rigorous methodology with Python-based execution, Multi-Omics Data Integration with Python provides a pathway to biological understanding at unprecedented resolution.
This book offers a comprehensive guide to multi-omics analysis using Python and open scientific tooling. Readers learn how to unify heterogeneous datasets, construct multi-layer models, map biological networks, and derive systems-level insights from complex molecular profiles. Through hands-on examples and end-to-end workflows, the book demonstrates practical techniques for feature harmonization, joint dimensionality reduction, tensor-based integration, regulatory inference, deep learning architectures for multi-modal fusion, and network-based interpretation frameworks.
Inside, you will learn how
• Load and harmonize genomic, transcriptomic, proteomic, and metabolomic datasets
• Perform cross-modal normalization, alignment, and missing data resolution
• Apply graphical, statistical, and machine learning approaches for multi-omic fusion
• Use network and pathway models to interpret regulatory interactions
• Deploy deep learning models for representation learning and phenotype prediction
• Integrate multi-omic data with EHR, imaging, and clinical metadata
• Build end-to-end pipelines for biomarker discovery and mechanistic insight
Designed for computational biologists, data scientists, systems biologists, and researchers in molecular medicine, this book provides both theoretical foundations and practical workflows that translate directly to research and industry applications. By combining rigorous methodology with Python-based execution, Multi-Omics Data Integration with Python provides a pathway to biological understanding at unprecedented resolution.
443 pages, Kindle Edition
Published January 10, 2026
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