Machine Learning Methods for Multi-Omics Data Integration Ebook Tooltip Ebooks kunnen worden gelezen op uw computer en op daarvoor geschikte e-readers.
Afbeeldingen
Sla de afbeeldingen overArtikel vergelijken
- Engels
- E-book
- 9783031365027
- 13 november 2023
- Adobe ePub
Samenvatting
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.
Productspecificaties
Inhoud
- Taal
- en
- Bindwijze
- E-book
- Oorspronkelijke releasedatum
- 13 november 2023
- Ebook Formaat
- Adobe ePub
Betrokkenen
- Hoofdredacteur
- Abedalrhman Alkhateeb
- Tweede Redacteur
- Luis Rueda
- Hoofduitgeverij
- Springer
Lees mogelijkheden
- Lees dit ebook op
- Desktop (Mac en Windows) | Kobo e-reader | Android (smartphone en tablet) | iOS (smartphone en tablet) | Windows (smartphone en tablet)
Overige kenmerken
- Studieboek
- Nee
EAN
- EAN
- 9783031365027
Je vindt dit artikel in
- Categorieën
- Taal
- Engels
- Beschikbaarheid
- Leverbaar
- Boek, ebook of luisterboek?
- Ebook
- Studieboek of algemeen
- Studieboeken
Kies gewenste uitvoering
Prijsinformatie en bestellen
De prijs van dit product is 165 euro.- E-book is direct beschikbaar na aankoop
- E-books lezen is voordelig
- Dag en nacht klantenservice
- Veilig betalen
Rapporteer dit artikel
Je wilt melding doen van illegale inhoud over dit artikel:
- Ik wil melding doen als klant
- Ik wil melding doen als autoriteit of trusted flagger
- Ik wil melding doen als partner
- Ik wil melding doen als merkhouder
Geen klant, autoriteit, trusted flagger, merkhouder of partner? Gebruik dan onderstaande link om melding te doen.