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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
Attention for Chapter 10: Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees
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Chapter title
Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees
Chapter number 10
Book title
Data Mining for Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8561-6_10
Pubmed ID
Book ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Authors

Kiyoko F. Aoki-Kinoshita, Aoki-Kinoshita, Kiyoko F.

Abstract

This chapter describes the Multiple Carbohydrate Alignment with Weights (MCAW) tool, which is available as a part of the RINGS (Resource for INformatics of Glycomes at Soka) website. It implements a combination of KCaM (Aoki, Yamaguchi, Ueda, et al., Nucl Acids Res 32:W267-W272, 2004), a pairwise glycan alignment algorithm, and ClustalW (Thompson, Higgins, Gibson, Nucleic Acids Res 22:4673-80, 1994), a weighted multiple protein sequence alignment algorithm. This tool computes the multiple glycan alignment by first computing a guide tree to determine the order by which to progressively add glycans to the multiple alignment. The dynamic programming algorithm results in a glycan profile of the alignment glycans, containing "monosaccharide positions" indicating the ratio of monosaccharides and their glycosidic bonds that are aligned at the corresponding position. This tool has been used to analyze databases of glycan array experimental data, incorporating weights to reflect the biological significance of certain glycans over others. As a result, it has been shown that the alignments obtained are biologically relevant, matching the results as found in the literature.

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Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 50%
Unknown 1 50%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 50%
Unknown 1 50%