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Data Mining for Systems Biology

Overview of attention for book
Cover of 'Data Mining for Systems Biology'

Table of Contents

  1. Altmetric Badge
    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
  4. Altmetric Badge
    Chapter 3 Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas
  5. Altmetric Badge
    Chapter 4 Generative Models for Quantification of DNA Modifications
  6. Altmetric Badge
    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
  11. Altmetric Badge
    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
  13. Altmetric Badge
    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 2: MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

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Chapter title
MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification
Chapter number 2
Book title
Data Mining for Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8561-6_2
Pubmed ID
Book ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Authors

Kévin Vervier, Pierre Mahé, Jean-Philippe Vert, Vervier, Kévin, Mahé, Pierre, Vert, Jean-Philippe

Abstract

Metagenomics is the study of microbial community diversity, especially the uncultured microorganisms by shotgun sequencing environmental samples. As the sequencers throughput and the data volume increase, it becomes challenging to develop scalable bioinformatics tools that reconstruct microbiome structure by binning sequencing reads to reference genomes. Standard alignment-based methods, such as BWA-MEM, provide state-of-the-art performance, but we demonstrate in Vervier et al. (2016) that compositional approaches using nucleotides motifs have faster analysis time, for comparable accuracy. In this work, we describe how to use MetaVW, a scalable machine learning implementation for short sequencing reads binning, based on their k-mers profile. We provide a step-by-step guideline on how we trained the classification models and how it can easily generalize to user-defined reference genomes and specific applications. We also give additional details on what effect parameters in the algorithm have on performances.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 28%
Student > Ph. D. Student 9 17%
Student > Master 8 15%
Student > Bachelor 4 8%
Student > Doctoral Student 3 6%
Other 5 9%
Unknown 9 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 26%
Agricultural and Biological Sciences 11 21%
Computer Science 9 17%
Medicine and Dentistry 4 8%
Chemistry 2 4%
Other 3 6%
Unknown 10 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 29 July 2018.
All research outputs
#3,105,887
of 24,093,053 outputs
Outputs from Methods in molecular biology
#614
of 13,601 outputs
Outputs of similar age
#69,147
of 449,849 outputs
Outputs of similar age from Methods in molecular biology
#37
of 1,482 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,601 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 95% of its peers.
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 449,849 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 1,482 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.