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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 17: Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG
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Chapter title
Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG
Chapter number 17
Book title
Data Mining for Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8561-6_17
Pubmed ID
30030815
Book ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Authors

Minoru Kanehisa, Kanehisa, Minoru

Abstract

The KEGG database is widely used as a reference knowledge base for biological interpretation of genome sequences and other high-throughput data. It contains, among others, KEGG pathway maps and BRITE hierarchies (ontologies) representing high-level systemic functions of the cell and the organism. By the processes called pathway mapping and BRITE mapping, information encoded in the genome, especially the repertoire of genes, is converted to such high-level functional information. This general methodology can be applied to microbial genomes to infer antimicrobial resistance (AMR), which is becoming an increasingly serious threat to the global public health. Here we present how knowledge on AMR is accumulated in the KEGG Pathogen resource and how such knowledge can be utilized by BlastKOALA and other web tools.

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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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Bachelor 2 14%
Student > Doctoral Student 1 7%
Student > Ph. D. Student 1 7%
Other 1 7%
Other 0 0%
Unknown 6 43%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 14%
Agricultural and Biological Sciences 2 14%
Medicine and Dentistry 2 14%
Social Sciences 1 7%
Chemistry 1 7%
Other 0 0%
Unknown 6 43%
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 06 March 2019.
All research outputs
#18,836,331
of 23,344,526 outputs
Outputs from Methods in molecular biology
#8,106
of 13,338 outputs
Outputs of similar age
#332,562
of 444,166 outputs
Outputs of similar age from Methods in molecular biology
#955
of 1,502 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,338 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 444,166 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,502 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.

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