0
Your cart

Your cart is empty

Browse All Departments
  • All Departments
Price
  • R5,000 - R10,000 (1)
  • -
Status
Brand

Showing 1 - 1 of 1 matches in All Departments

Machine Learning Methods for Multi-Omics Data Integration (1st ed. 2023): Abedalrhman Alkhateeb, Luis Rueda Machine Learning Methods for Multi-Omics Data Integration (1st ed. 2023)
Abedalrhman Alkhateeb, Luis Rueda
R5,586 Discovery Miles 55 860 Ships in 10 - 15 working days

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.

Free Delivery
Pinterest Twitter Facebook Google+
You may like...
The Occult Sciences - A Compendium of…
Arthur Edward Waite Hardcover R872 Discovery Miles 8 720
Lost Keys of Freemasonry - The Legend of…
Manly P Hall Hardcover R664 R591 Discovery Miles 5 910
Thumb Position for Cello, Book 1
Rick Mooney Staple bound R328 R305 Discovery Miles 3 050
Suzuki Violin School 7 - Piano Acc…
Shinichi Suzuki Book R235 Discovery Miles 2 350
Suzuki Violin School, Volume 5 - Violin…
Shinichi Suzuki, Augustin Hadelich, … Paperback R654 Discovery Miles 6 540
Guitar Music by Women Composers - An…
Kristan Aspen, Janna MacAuslan Hardcover R2,087 Discovery Miles 20 870
Grit - Why Passion & Resilience Are The…
Angela Duckworth Paperback  (3)
R325 R300 Discovery Miles 3 000
Memoir of Johann Gottlieb Fichte
William Smith Paperback R488 Discovery Miles 4 880
Making the Climb - What a Novice Climber…
John C Bowling Paperback R376 R353 Discovery Miles 3 530
The Mountain Is You - Transforming…
Brianna Wiest Paperback R599 R532 Discovery Miles 5 320

 

Partners