↓ Skip to main content

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
  7. Altmetric Badge
    Chapter 6 Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language
  8. Altmetric Badge
    Chapter 7 Multiple Testing Tool to Detect Combinatorial Effects in Biology
  9. Altmetric Badge
    Chapter 8 SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining
  10. Altmetric Badge
    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 3: Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
7 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas
Chapter number 3
Book title
Data Mining for Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8561-6_3
Pubmed ID
Book ISBNs
978-1-4939-8560-9, 978-1-4939-8561-6
Authors

Jesper Lund, Qihua Tan, Jan Baumbach, Lund, Jesper, Tan, Qihua, Baumbach, Jan

Abstract

In recent decades, the accumulation of data on 16s ribosomal RNA genes has yielded free and public databases such as SILVA, GreenGenes, The Ribosomal Database Project, and IMG, handling massive amounts of raw data and meta information. 16s rRNA gene contains hypervariable regions with great classification power. As a result, numerous classification tools have emerged including state-of-the-art tools such as Mothur, Qiime, and the 16s classifier. However, there is a gap between the sequence databases, the taxonomy profiling tools and available meta information such as geo/body-location information. Here, we present BioAtlas, and interactive web tool for searching, exploring, and analyzing prokaryotic distributions by integration of various resources of metagenomics databases. In the following section we show how to use BioAtlas to (1) search and explore prokaryote occurrences across the geospatial map of the world, (2) investigate and hunt for occurrences across generic user-generated surface-specific maps, with an example map of a human female, with data from Bouslimani et al., and (3) classify a user-given sequences dataset through our online platform for visual exploration of the spatial abundances of the identified microbes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 43%
Researcher 1 14%
Unknown 3 43%
Readers by discipline Count As %
Computer Science 2 29%
Biochemistry, Genetics and Molecular Biology 1 14%
Agricultural and Biological Sciences 1 14%
Unknown 3 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 July 2018.
All research outputs
#14,421,028
of 23,096,849 outputs
Outputs from Methods in molecular biology
#4,241
of 13,208 outputs
Outputs of similar age
#240,864
of 442,670 outputs
Outputs of similar age from Methods in molecular biology
#432
of 1,499 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,208 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 64% 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 442,670 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
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 has gotten more attention than average, scoring higher than 66% of its contemporaries.