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Data Mining Techniques for the Life Sciences

Overview of attention for book
Cover of 'Data Mining Techniques for the Life Sciences'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Data Mining Techniques for the Life Sciences
  3. Altmetric Badge
    Chapter 2 Protein Structure Databases.
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    Chapter 3 The MIntAct Project and Molecular Interaction Databases.
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    Chapter 4 Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
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    Chapter 5 Classification and Exploration of 3D Protein Domain Interactions Using Kbdock.
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    Chapter 6 Data Mining of Macromolecular Structures.
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    Chapter 7 Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.
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    Chapter 8 Homology-Based Annotation of Large Protein Datasets.
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    Chapter 9 Data Mining Techniques for the Life Sciences
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    Chapter 10 Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps of Protein Assemblies Using Evolutionary Information from Aligned Homologous Proteins.
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    Chapter 11 Systematic Exploration of an Efficient Amino Acid Substitution Matrix: MIQS.
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    Chapter 12 Promises and Pitfalls of High-Throughput Biological Assays.
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    Chapter 13 Data Mining Techniques for the Life Sciences
  15. Altmetric Badge
    Chapter 14 Predicting Conformational Disorder.
  16. Altmetric Badge
    Chapter 15 Classification of Protein Kinases Influenced by Conservation of Substrate Binding Residues.
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    Chapter 16 Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence.
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    Chapter 17 Protein Crystallizability.
  19. Altmetric Badge
    Chapter 18 Data Mining Techniques for the Life Sciences
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    Chapter 19 Data Mining Techniques for the Life Sciences
  21. Altmetric Badge
    Chapter 20 Functional Analysis of Metabolomics Data.
  22. Altmetric Badge
    Chapter 21 Data Mining Techniques for the Life Sciences
  23. Altmetric Badge
    Chapter 22 A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.
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    Chapter 23 Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
  25. Altmetric Badge
    Chapter 24 Protein Residue Contacts and Prediction Methods.
  26. Altmetric Badge
    Chapter 25 The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.
  27. Altmetric Badge
    Chapter 26 Data Mining Techniques for the Life Sciences
  28. Altmetric Badge
    Chapter 27 Data Mining Techniques for the Life Sciences
Attention for Chapter 26: Data Mining Techniques for the Life Sciences
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
15 tweeters
googleplus
1 Google+ user

Citations

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7 Dimensions

Readers on

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54 Mendeley
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Chapter title
Data Mining Techniques for the Life Sciences
Chapter number 26
Book title
Data Mining Techniques for the Life Sciences
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3572-7_26
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Fabijanić, Maja, Vlahoviček, Kristian, Maja Fabijanić, Kristian Vlahoviček

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

Metagenomics projects use next-generation sequencing to unravel genetic potential in microbial communities from a wealth of environmental niches, including those associated with human body and relevant to human health. In order to understand large datasets collected in metagenomics surveys and interpret them in context of how a community metabolism as a whole adapts and interacts with the environment, it is necessary to extend beyond the conventional approaches of decomposing metagenomes into microbial species' constituents and performing analysis on separate components. By applying concepts of translational optimization through codon usage adaptation on entire metagenomic datasets, we demonstrate that a bias in codon usage present throughout the entire microbial community can be used as a powerful analytical tool to predict for community lifestyle-specific metabolism. Here we demonstrate this approach combined with machine learning, to classify human gut microbiome samples according to the pathological condition diagnosed in the human host.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Brazil 2 4%
Unknown 50 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 20%
Student > Postgraduate 7 13%
Student > Ph. D. Student 6 11%
Student > Master 6 11%
Student > Bachelor 5 9%
Other 11 20%
Unknown 8 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 20%
Biochemistry, Genetics and Molecular Biology 10 19%
Computer Science 8 15%
Medicine and Dentistry 5 9%
Immunology and Microbiology 4 7%
Other 4 7%
Unknown 12 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 14 December 2017.
All research outputs
#1,930,645
of 22,489,683 outputs
Outputs from Methods in molecular biology
#303
of 12,924 outputs
Outputs of similar age
#38,761
of 409,526 outputs
Outputs of similar age from Methods in molecular biology
#42
of 1,266 outputs
Altmetric has tracked 22,489,683 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,924 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done particularly well, scoring higher than 97% 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 409,526 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 1,266 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 96% of its contemporaries.