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Computational Intelligence Techniques for Comparative Genomics

Overview of attention for book
Cover of 'Computational Intelligence Techniques for Comparative Genomics'

Table of Contents

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    Book Overview
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    Chapter 1 Diversified Insulin-Associated Beta-Behavioral and Endogenously Triggered Exposed Symptoms (DIABETES) Model of Diabetes in India
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    Chapter 2 Automatic Teaching–Learning-Based Optimization: A Novel Clustering Method for Gene Functional Enrichments
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    Chapter 3 A Comparative Study of Methodologies of Protein Secondary Structure
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    Chapter 4 A Sparse-modeled ROI for GLAM Construction in Image Classification Problems—A Case Study of Breast Cancer
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    Chapter 5 A Survey on Identification of Protein Complexes in Protein–protein Interaction Data: Methods and Evaluation
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    Chapter 6 Modeling Artificial Life: A Cellular Automata Approach
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    Chapter 7 Identification of Deleterious SNPs in TACR1 Gene Using Genetic Algorithm
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    Chapter 8 Identification of AIDS Disease Severity Using Genetic Algorithm
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    Chapter 9 A Novel Clustering Approach Using Hadoop Distributed Environment
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    Chapter 10 Framework for Evaluation of Programming Language Examinations
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    Chapter 11 An Efficient Data Integration Framework in Cloud Using MapReduce
Attention for Chapter 6: Modeling Artificial Life: A Cellular Automata Approach
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Chapter title
Modeling Artificial Life: A Cellular Automata Approach
Chapter number 6
Book title
Computational Intelligence Techniques for Comparative Genomics
Published by
Springer Singapore, January 2015
DOI 10.1007/978-981-287-338-5_6
Book ISBNs
978-9-81-287337-8, 978-9-81-287338-5
Authors

Kunjam Nageswara Rao, Madugula Divya, M. Pallavi, B. Naga Priyanka

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 50%
Lecturer 1 50%
Readers by discipline Count As %
Computer Science 2 100%

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