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Artificial intelligence technologies for computational biology / edited by Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, A.L. Sangal.

Contributor(s): Material type: TextPublisher: Boca Raton : CRC Press, 2023Edition: First editionDescription: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781003246688
  • 1003246680
  • 9781000778687
  • 1000778681
  • 9781000778694
  • 100077869X
Subject(s): DDC classification:
  • 570.285 23/eng/20220808
LOC classification:
  • QH324.25
Online resources: Summary: "This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing. It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems. The text is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology. This book Covers algorithms in the fields of artificial intelligence, and machine learning useful in biological data analysis. Discusses comprehensively artificial intelligence and automatic image interpretation in modern biological systems. Presents the application of evolutionary computations for fractal visualization of sequence data. Explores the use of genetic algorithms for pair-wise and multiple sequence alignments. Examines the roles of efficient computational techniques in biology"-- Provided by publisher.
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"This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing. It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems. The text is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology. This book Covers algorithms in the fields of artificial intelligence, and machine learning useful in biological data analysis. Discusses comprehensively artificial intelligence and automatic image interpretation in modern biological systems. Presents the application of evolutionary computations for fractal visualization of sequence data. Explores the use of genetic algorithms for pair-wise and multiple sequence alignments. Examines the roles of efficient computational techniques in biology"-- Provided by publisher.

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