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Machine Learning Methods for Multi-Omics Data Integration by Luis Rueda Hardcove
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- Buy it nowMachine Learning Methods for Multi-Omics Data Integration by Luis Rueda Hardcove
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Located in: Calgary, Alberta, Canada
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eBay item number:364901147718
Item specifics
- Condition
- ISBN-13
- 9783031365010
- Book Title
- Machine Learning Methods for Multi-Omics Data Integration
- ISBN
- 9783031365010
- Subject Area
- Computers, Mathematics
- Publication Name
- Machine Learning Methods for Multi-Omics Data Integration
- Item Length
- 9.3 in
- Publisher
- Springer International Publishing A&G
- Subject
- Probability & Statistics / General, Intelligence (Ai) & Semantics, General, Bioinformatics
- Publication Year
- 2023
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Width
- 6.1 in
- Item Weight
- 15.4 Oz
- Number of Pages
- VI, 168 Pages
About this product
Product Information
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 integratingthese 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.
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3031365011
ISBN-13
9783031365010
eBay Product ID (ePID)
7060734930
Product Key Features
Publication Name
Machine Learning Methods for Multi-Omics Data Integration
Format
Hardcover
Language
English
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, General, Bioinformatics
Publication Year
2023
Type
Textbook
Subject Area
Computers, Mathematics
Number of Pages
VI, 168 Pages
Dimensions
Item Length
9.3 in
Item Width
6.1 in
Item Weight
15.4 Oz
Additional Product Features
Number of Volumes
1 Vol.
Lc Classification Number
Qh324.2-324.25
Table of Content
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.
Copyright Date
2024
Dewey Decimal
005.73
Dewey Edition
23
Illustrated
Yes
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Premier Books LLC
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eBay item number:364901147718
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