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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.
  4. Altmetric Badge
    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.
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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 21: 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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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1 blog
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7 X users

Citations

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Chapter title
Data Mining Techniques for the Life Sciences
Chapter number 21
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_21
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Orsini, Massimiliano, Cuccuru, Gianmauro, Uva, Paolo, Fotia, Giorgio, Massimiliano Orsini, Gianmauro Cuccuru, Paolo Uva, Giorgio Fotia

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

Bacterial genome sequencing is now an affordable choice for many laboratories for applications in research, diagnostic, and clinical microbiology. Nowadays, an overabundance of tools is available for genomic data analysis. However, tools differ for algorithms, languages, hardware requirements, and user interface, and combining them as it is necessary for sequence data interpretation often requires (bio)informatics skills which can be difficult to find in many laboratories. In addition, multiple data sources, as well as exceedingly large dataset sizes, and increasingly computational complexity further challenge the accessibility, reproducibility, and transparency of the entire process. In this chapter we will cover the main bioinformatics steps required for a complete bacterial genome analysis using next-generation sequencing data, from the raw sequence data to assembled and annotated genomes. All the tools described are available in the Orione framework ( http://orione.crs4.it ), which uniquely combines in a transparent way the most used open source bioinformatics tools for microbiology, allowing microbiologist without any specific hardware or informatics skill to conduct data-intensive computational analyses from quality control to microbial gene annotation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 23%
Student > Master 5 19%
Researcher 5 19%
Other 2 8%
Student > Ph. D. Student 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 31%
Biochemistry, Genetics and Molecular Biology 5 19%
Computer Science 3 12%
Immunology and Microbiology 2 8%
Engineering 2 8%
Other 2 8%
Unknown 4 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 12 May 2016.
All research outputs
#2,696,875
of 22,865,319 outputs
Outputs from Methods in molecular biology
#507
of 13,127 outputs
Outputs of similar age
#48,253
of 393,648 outputs
Outputs of similar age from Methods in molecular biology
#80
of 1,470 outputs
Altmetric has tracked 22,865,319 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 96% 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 393,648 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 87% of its contemporaries.
We're also able to compare this research output to 1,470 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 94% of its contemporaries.