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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
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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.
  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.
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    Chapter 26 Data Mining Techniques for the Life Sciences
  28. Altmetric Badge
    Chapter 27 Data Mining Techniques for the Life Sciences
Attention for Chapter 23: Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
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Citations

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Chapter title
Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
Chapter number 23
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_23
Pubmed ID
27115647
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Salvatore Alaimo, Rosalba Giugno, Alfredo Pulvirenti

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

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Twitter Demographics

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 22%
Researcher 6 16%
Student > Master 4 11%
Lecturer 2 5%
Other 2 5%
Other 5 14%
Unknown 10 27%
Readers by discipline Count As %
Computer Science 8 22%
Engineering 3 8%
Agricultural and Biological Sciences 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Medicine and Dentistry 2 5%
Other 6 16%
Unknown 13 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 January 2017.
All research outputs
#18,463,662
of 22,877,793 outputs
Outputs from Methods in molecular biology
#7,924
of 13,131 outputs
Outputs of similar age
#284,559
of 393,697 outputs
Outputs of similar age from Methods in molecular biology
#845
of 1,471 outputs
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So far Altmetric has tracked 13,131 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1,471 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.

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