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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
  2. Altmetric Badge
    Chapter 1 Identifying Bacterial Strains from Sequencing Data
  3. Altmetric Badge
    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
  5. Altmetric Badge
    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
  12. Altmetric Badge
    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
  14. Altmetric Badge
    Chapter 13 Sparse Modeling to Analyze Drug–Target Interaction Networks
  15. Altmetric Badge
    Chapter 14 DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank
  16. Altmetric Badge
    Chapter 15 MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
  17. Altmetric Badge
    Chapter 16 Disease Gene Classification with Metagraph Representations
  18. Altmetric Badge
    Chapter 17 Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG
Attention for Chapter 9: Computing and Visualizing Gene Function Similarity and Coherence with NaviGO
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Chapter title
Computing and Visualizing Gene Function Similarity and Coherence with NaviGO
Chapter number 9
Book title
Data Mining for Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8561-6_9
Pubmed ID
Book ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Authors

Ziyun Ding, Qing Wei, Daisuke Kihara, Ding, Ziyun, Wei, Qing, Kihara, Daisuke

Abstract

Gene ontology (GO) is a controlled vocabulary of gene functions across all species, which is widely used for functional analyses of individual genes and large-scale proteomic studies. NaviGO is a webserver for visualizing and quantifying the relationship and similarity of GO annotations. Here, we walk through functionality of the NaviGO webserver ( http://kiharalab.org/web/navigo/ ) using an example input and explain what can be learned from analysis results. NaviGO has four main functions, accessed from each page of the webserver: "GO Parents," "GO Set", "GO Enrichment", and "Protein Set." For a given list of GO terms, the "GO Parents" tab visualizes the hierarchical relationship of GO terms, and the "GO Set" tab calculates six functional similarity and association scores and presents results in a network and a multidimensional scaling plot. For a set of proteins and their associated GO terms, the "GO Enrichment" tab calculates protein GO functional enrichment, while the "Protein Set" tab calculates functional association between proteins. The NaviGO source code can be also downloaded and used locally or integrated into other software pipelines.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 27%
Student > Ph. D. Student 2 18%
Student > Bachelor 2 18%
Researcher 2 18%
Unspecified 1 9%
Other 0 0%
Unknown 1 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 27%
Agricultural and Biological Sciences 2 18%
Computer Science 2 18%
Pharmacology, Toxicology and Pharmaceutical Science 1 9%
Unspecified 1 9%
Other 1 9%
Unknown 1 9%
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 25 July 2018.
All research outputs
#15,692,595
of 23,318,744 outputs
Outputs from Methods in molecular biology
#5,494
of 13,323 outputs
Outputs of similar age
#271,611
of 444,001 outputs
Outputs of similar age from Methods in molecular biology
#601
of 1,502 outputs
Altmetric has tracked 23,318,744 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,323 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% 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 444,001 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% 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 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.