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Handbook of Research on Computational Intelligence Applications in Bioinformatics

Handbook of Research on Computational Intelligence Applications in Bioinformatics

Sujata Dash, Bidyadhar Subudhi
Copyright: © 2016 |Pages: 514
ISBN13: 9781522504276|ISBN10: 1522504273|EISBN13: 9781522504283
DOI: 10.4018/978-1-5225-0427-6
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MLA

Dash, Sujata, and Bidyadhar Subudhi, editors. Handbook of Research on Computational Intelligence Applications in Bioinformatics. IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0427-6

APA

Dash, S. & Subudhi, B. (Eds.). (2016). Handbook of Research on Computational Intelligence Applications in Bioinformatics. IGI Global. https://doi.org/10.4018/978-1-5225-0427-6

Chicago

Dash, Sujata, and Bidyadhar Subudhi, eds. Handbook of Research on Computational Intelligence Applications in Bioinformatics. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0427-6

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Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings.

The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.

Table of Contents

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Front Materials
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Title Page
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Copyright Page
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Advances in Bioinformatics and Biomedical Engineering (ABBE) Book Series
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Editorial Advisory Board and List of Reviewers
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Preface
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Acknowledgment
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Chapters
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Chapter 1
Section 1 embodies four chapters which primarily focus on the data-mining techniques to deal with enormous amount of data. Methods to address the problem inherent to micro-array datasets i.e., curse of dimensionality are presented in...
Big Data Mining and Pattern Discovery
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Chapter 2
This section explains the development various intelligent computational models for predicting complex biological problems. A complex novel hybrid model using radial basis function neural network and multi-objective algorithm based...
Computational Intelligence in Bioinformatics
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Chapter 3
In this section many nature inspired computing algorithms are discussed for analyzing and predicting protein and micro-array data. Five chapters are included in this section. Various computational approaches for predicting...
Nature-Inspired Computing for Analysis of DNA and Protein Microarray Data
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Chapter 4
This section gives in-depth information about bio-inspired algorithms and its application in finding optimized solutions for engineering problems. Different evolutionary computing techniques are discussed to identify complex systems...
Bio-Inspired Algorithms and Engineering Applications
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Back Materials
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Compilation of References
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About the Contributors
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Index
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