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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 Nucleic Acid Sequence and Structure Databases
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    Chapter 2 Genomic Databases and Resources at the National Center for Biotechnology Information
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    Chapter 3 Protein Sequence Databases
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    Chapter 4 Protein Structure Databases
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    Chapter 5 Protein Domain Architectures
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    Chapter 6 Thermodynamic Database for Proteins: Features and Applications
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    Chapter 7 Enzyme databases.
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    Chapter 8 Biomolecular Pathway Databases
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    Chapter 9 Databases of Protein–Protein Interactions and Complexes
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    Chapter 10 Proximity Measures for Cluster Analysis
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    Chapter 11 Clustering Criteria and Algorithms
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    Chapter 12 Neural networks.
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    Chapter 13 A User’s Guide to Support Vector Machines
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    Chapter 14 Data Mining Techniques for the Life Sciences
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    Chapter 15 Integrated Tools for Biomolecular Sequence-Based Function Prediction as Exemplified by the ANNOTATOR Software Environment
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    Chapter 16 Computational Methods for Ab Initio and Comparative Gene Finding
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    Chapter 17 Sequence and structure analysis of noncoding RNAs.
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    Chapter 18 Conformational Disorder
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    Chapter 19 Protein secondary structure prediction.
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    Chapter 20 Analysis and Prediction of Protein Quaternary Structure
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    Chapter 21 Prediction of Posttranslational Modification of Proteins from Their Amino Acid Sequence
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    Chapter 22 Protein Crystallizability
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Mentioned by

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2 X users
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2 patents
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8 Wikipedia pages

Citations

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29 Dimensions

Readers on

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531 Mendeley
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7 CiteULike
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Title
Data Mining Techniques for the Life Sciences
Published by
Humana Press, December 2009
DOI 10.1007/978-1-60327-241-4
ISBNs
978-1-60327-240-7, 978-1-60327-241-4, 978-1-4939-5688-3
Editors

Carugo, Oliviero, Eisenhaber, Frank

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 531 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 531 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 <1%
Other 2 <1%
Student > Ph. D. Student 2 <1%
Student > Postgraduate 2 <1%
Student > Master 2 <1%
Other 3 <1%
Unknown 517 97%
Readers by discipline Count As %
Computer Science 4 <1%
Biochemistry, Genetics and Molecular Biology 2 <1%
Engineering 2 <1%
Mathematics 1 <1%
Business, Management and Accounting 1 <1%
Other 3 <1%
Unknown 518 98%