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Data Mining for Systems Biology

Overview of attention for book
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 8: SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining
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Chapter title
SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining
Chapter number 8
Book title
Data Mining for Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8561-6_8
Pubmed ID
30030806
Book ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Authors

Kei-ichiro Takahashi, David A. duVerle, Sohiya Yotsukura, Ichigaku Takigawa, Hiroshi Mamitsuka, Takahashi, Kei-ichiro, duVerle, David A., Yotsukura, Sohiya, Takigawa, Ichigaku, Mamitsuka, Hiroshi

Abstract

Biclustering extracts coexpressed genes under certain experimental conditions, providing more precise insight into the genetic behaviors than one-dimensional clustering. For understanding the biological features of genes in a single bicluster, visualizations such as heatmaps or parallel coordinate plots and tools for enrichment analysis are widely used. However, simultaneously handling many biclusters still remains a challenge. Thus, we developed a web service named SiBIC, which, using maximal frequent itemset mining, exhaustively discovers significant biclusters, which turn into networks of overlapping biclusters, where nodes are gene sets and edges show their overlaps in the detected biclusters. SiBIC provides a graphical user interface for manipulating a gene set network, where users can find target gene sets based on the enriched network. This chapter provides a user guide/instruction of SiBIC with background of having developed this software. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/sibic/faces/index.jsp .

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X Demographics

The data shown below were collected from the profile of 1 X user 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 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%
Student > Master 1 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 50%
Agricultural and Biological Sciences 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2018.
All research outputs
#20,527,576
of 23,096,849 outputs
Outputs from Methods in molecular biology
#9,977
of 13,208 outputs
Outputs of similar age
#378,510
of 442,670 outputs
Outputs of similar age from Methods in molecular biology
#1,194
of 1,499 outputs
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So far Altmetric has tracked 13,208 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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