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Data Mining for Systems Biology Methods and Protocols
Title
Data Mining for Systems Biology [electronic resource] : Methods and Protocols / edited by Hiroshi Mamitsuka.
ISBN
9781493985616
Edition
2nd ed. 2018.
Publication
New York, NY : Springer New York : Imprint: Humana Press, 2018.
Physical Description
1 online resource (XI, 243 p.) 95 illus., 86 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer protocols (Series)
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
August 31, 2018
Series
Methods in Molecular Biology, 1807
Contents
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.
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