Machine Learning Methods for Multi-Omics Data Integration / edited by Abedalrhman Alkhateeb, Luis Rueda.

Format:

Book

Contributor:

Alkhateeb, Abedalrhman.

Rueda, Luis.

Series:

Computer Science Series

Language:

English

Subjects (All):

Bioinformatics.

Machine learning.

Data mining.

Medical informatics.

Machine Learning.

Data Mining and Knowledge Discovery.

Health Informatics.

Computational and Systems Biology.

Local Subjects:

Bioinformatics.

Machine Learning.

Data Mining and Knowledge Discovery.

Health Informatics.

Computational and Systems Biology.

Physical Description:

vi, 168 p.

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2024.

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.

Contents:

Chapter 1: Introduction to Multiomics Technology, Ahmed Hajyasien

Chapter 2: Multi-omics Data Integration Applications and Structures, Ammar El-Hassa

Chapter 3: Machine learning approaches for multi-omics data integration in medicine, Fatma Hilal Yagin

Chapter 4: Multimodal methods for knowledge discovery from bulk and single-cell multi-omics data, Yue Li, Gregory Fonseca, and Jun Ding

Chapter 5: Negative sample selection for miRNA-disease association prediction models, Yulian Ding, Fei Wang, Yuchen Zhang, Fang-Xiang Wu

Chapter 6: Prediction and Analysis of Key Genes in Prostate Cancer via MRMR Enhanced Similarity Preserving Criteria and Pathway Enrichment Methods, Robert Benjamin Eshun, Hugette Naa Ayele Aryee, Marwan U. Bikdash, and A.K.M Kamrul Islam

Chapter 7: Graph-Based Machine Learning Approaches for Pangenomics, Indika Kahanda, Joann Mudge, Buwani Manuweera, Thiruvarangan Ramaraj, Alan Cleary, and Brendan Mumey

Chapter 8: Multiomics-based tensor decomposition for characterizing breast cancer heterogeneity,

Qian Liu, Shujun Huang, Zhongyuan Zhang, Ted M. Lakowski, Wei Xu and Pingzhao Hu

Chapter 9: Multi-Omics Databases, Hania AlOmari, Abedalrhman Alkhateeb, and Bassam Hammo.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Alkhateeb, Abedalrhman Machine Learning Methods for Multi-Omics Data Integration

ISBN:

9783031365027

9783031365010

303136502X

OCLC:

1409686591