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Data Mining for Systems Biology

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Cover of 'Data Mining for Systems Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Identifying Bacterial Strains from Sequencing Data
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    Chapter 2 MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification
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    Chapter 3 Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas
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    Chapter 4 Generative Models for Quantification of DNA Modifications
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    Chapter 5 DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data
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    Chapter 6 Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language
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    Chapter 7 Multiple Testing Tool to Detect Combinatorial Effects in Biology
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    Chapter 8 SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining
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    Chapter 9 Computing and Visualizing Gene Function Similarity and Coherence with NaviGO
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    Chapter 10 Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees
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    Chapter 11 Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis
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    Chapter 12 Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing
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    Chapter 13 Sparse Modeling to Analyze Drug–Target Interaction Networks
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    Chapter 14 DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank
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    Chapter 15 MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
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    Chapter 16 Disease Gene Classification with Metagraph Representations
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    Chapter 17 Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG
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Title
Data Mining for Systems Biology
Published by
Springer New York, January 2018
DOI 10.1007/978-1-4939-8561-6
ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Editors

Hiroshi Mamitsuka

X Demographics

X Demographics

The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Student > Postgraduate 2 22%
Lecturer 1 11%
Researcher 1 11%
Unknown 2 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 11%
Mathematics 1 11%
Computer Science 1 11%
Immunology and Microbiology 1 11%
Medicine and Dentistry 1 11%
Other 0 0%
Unknown 4 44%