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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
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    Chapter 14 Predicting Conformational Disorder.
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    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.
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    Chapter 18 Data Mining Techniques for the Life Sciences
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    Chapter 19 Data Mining Techniques for the Life Sciences
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    Chapter 20 Functional Analysis of Metabolomics Data.
  22. Altmetric Badge
    Chapter 21 Data Mining Techniques for the Life Sciences
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    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.
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    Chapter 24 Protein Residue Contacts and Prediction Methods.
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    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 13: 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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

blogs
1 blog
twitter
16 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
144 Mendeley
Summary Blogs Twitter Dimensions citations
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Chapter title
Data Mining Techniques for the Life Sciences
Chapter number 13
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_13
Pubmed ID
27115637
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Dobin, Alexander, Gingeras, Thomas R, Alexander Dobin, Thomas R. Gingeras

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

Recent advances in high-throughput sequencing technology made it possible to probe the cell transcriptomes by generating hundreds of millions of short reads which represent the fragments of the transcribed RNA molecules. The first and the most crucial task in the RNA-seq data analysis is mapping of the reads to the reference genome. STAR (Spliced Transcripts Alignment to a Reference) is an RNA-seq mapper that performs highly accurate spliced sequence alignment at an ultrafast speed. STAR alignment algorithm can be controlled by many user-defined parameters. Here, we describe the most important STAR options and parameters, as well as best practices for achieving the maximum mapping accuracy and speed.

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Twitter Demographics

The data shown below were collected from the profiles of 16 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 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Italy 1 <1%
New Zealand 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Belgium 1 <1%
Unknown 136 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 25%
Student > Ph. D. Student 31 22%
Student > Master 14 10%
Student > Bachelor 9 6%
Student > Doctoral Student 7 5%
Other 15 10%
Unknown 32 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 49 34%
Agricultural and Biological Sciences 33 23%
Computer Science 5 3%
Medicine and Dentistry 5 3%
Neuroscience 4 3%
Other 15 10%
Unknown 33 23%

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 03 May 2016.
All research outputs
#1,947,162
of 23,577,654 outputs
Outputs from Methods in molecular biology
#283
of 13,410 outputs
Outputs of similar age
#35,040
of 397,072 outputs
Outputs of similar age from Methods in molecular biology
#38
of 1,472 outputs
Altmetric has tracked 23,577,654 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 13,410 research outputs from this source. They receive a mean Attention Score of 3.4. 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 397,072 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 91% of its contemporaries.
We're also able to compare this research output to 1,472 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.

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