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Data Mining Techniques for the Life Sciences

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Cover of 'Data Mining Techniques for the Life Sciences'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Data Mining Techniques for the Life Sciences
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    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.
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    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.
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    Chapter 26 Data Mining Techniques for the Life Sciences
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    Chapter 27 Data Mining Techniques for the Life Sciences
Attention for Chapter 22: A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.
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Chapter title
A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.
Chapter number 22
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_22
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Stefano Castellana, Caterina Fusilli, Tommaso Mazza

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

Next-generation sequencing has provided extraordinary opportunities to investigate the massive human genetic variability. It helped identifying several kinds of genomic mismatches from the wild-type reference genome sequences and to explain the onset of several pathogenic phenotypes and diseases susceptibility. In this context, distinguishing pathogenic from functionally neutral amino acid changes turns out to be a task as useful as complex, expensive, and time-consuming.Here, we present an exhaustive and up-to-dated survey of the algorithms and software packages conceived for the estimation of the putative pathogenicity of mutations, along with a description of the most popular mutation datasets that these tools used as training sets. Finally, we present and describe software for the prediction of cancer-related mutations.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Other 5 24%
Student > Ph. D. Student 5 24%
Student > Master 5 24%
Researcher 2 10%
Professor > Associate Professor 2 10%
Other 0 0%
Unknown 2 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 33%
Agricultural and Biological Sciences 3 14%
Medicine and Dentistry 2 10%
Nursing and Health Professions 2 10%
Mathematics 1 5%
Other 2 10%
Unknown 4 19%