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.
General
Imprint: |
Springer International Publishing AG
|
Country of origin: |
Switzerland |
Release date: |
October 2023 |
First published: |
2023 |
Editors: |
Abedalrhman Alkhateeb
• Luis Rueda
|
Dimensions: |
235 x 155mm (L x W) |
Pages: |
314 |
Edition: |
1st ed. 2023 |
ISBN-13: |
978-3-03-136501-0 |
Categories: |
Books
|
LSN: |
3-03-136501-1 |
Barcode: |
9783031365010 |
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