Data mining for systems biology methods and protocols
Data mining for systems biology: methods and protocols

This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowle...

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Bibliographische Detailangaben
Weitere Verfasser: Mamitsuka, Hiroshi ca. 20./21. Jh (HerausgeberIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: New York, NY Springer New York 2018
Ausgabe:2nd ed. 2018
Schriftenreihe:Methods in Molecular Biology
1807
Schlagworte:
Bioinformatics
Data Mining
Systembiologie
Bioinformatik
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Online-Zugang:UBR01
TUM01
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Zusammenfassung:This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency
Beschreibung:Identifying Bacterial Strains from Sequencing Data -- MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification -- Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas -- Generative Models for Quantification of DNA Modifications -- DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data -- Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language -- Multiple Testing Tool to Detect Combinatorial Effects in Biology -- SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining -- Computing and Visualizing Gene Function Similarity and Coherence with NaviGO -- Analyzing Glycan Binding Profiles Using Weighted Multiple Alignment of Trees -- Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis -- Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing -- Sparse Modeling to Analyze Drug-Target Interaction Networks -- DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank -- MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing -- Disease Gene Classification with Metagraph Representations -- Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG.
Beschreibung:1 Online-Ressource (XI, 243 Seiten) Illustrationen
ISBN:9781493985616
DOI:10.1007/978-1-4939-8561-6

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