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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
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    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 20: Functional Analysis of Metabolomics Data.
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Chapter title
Functional Analysis of Metabolomics Data.
Chapter number 20
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_20
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Mónica Chagoyen, Javier López-Ibáñez, Florencio Pazos

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 7%
Brazil 1 7%
Unknown 12 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Student > Ph. D. Student 4 29%
Student > Master 3 21%
Lecturer 1 7%
Student > Bachelor 1 7%
Other 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 36%
Biochemistry, Genetics and Molecular Biology 4 29%
Computer Science 2 14%
Medicine and Dentistry 1 7%
Chemistry 1 7%
Other 0 0%
Unknown 1 7%