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Statistical modelling and machine le...

Manisekhar, S. R.

Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applicationsedited by K. G. Srinivasa, G. M. Siddesh, S. R. Manisekhar.
other author:	Srinivasa, K. G.
Published:	Singapore :Springer Singapore :2020.
Description:	xii, 317 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computational biology.
Online resource:	https://doi.org/10.1007/978-981-15-2445-5
ISBN:	9789811524455$q(electronic bk.)

Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications
Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications[electronic resource] /edited by K. G. Srinivasa, G. M. Siddesh, S. R. Manisekhar. - Singapore :Springer Singapore :2020. - xii, 317 p. :ill., digital ;24 cm. - Algorithms for intelligent systems,2524-7565. - Algorithms for intelligent systems..

Part 1: Bioinformatics -- Chapter 1. Introduction to Bioinformatics -- Chapter 2. Review about Bioinformatics, Databases, Sequence Alignment, Docking and Drug Discovery -- Chapter 3. Machine Learning for Bioinformatics -- Chapter 4. Impact of Machine Learning in Bioinformatics Research -- Chapter 5. Text-mining in Bioinformatics -- Chapter 6. Open Source Software Tools for Bioinformatics -- Part 2: Protein Structure Prediction and Gene Expression Analysis -- Chapter 7. A Study on Protein Structure Prediction -- Chapter 8. Computational Methods Used in Prediction of Protein Structure -- Chapter 9. Computational Methods for Inference of Gene Regulatory Networks from Gene Expression Data -- Chapter 10. Machine Learning Algorithms for Feature Selection from Gene Expression Data -- Part 3: Genomics and Proteomics -- Chapter 11. Unsupervised Techniques in Genomics -- Chapter 12. Supervised Techniques in Proteomics -- Chapter 13. Visualizing Codon Usage Within and Across Genomes: Concepts and Tools -- Chapter 14. Single-Cell Multiomics: Dissecting Cancer.

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

ISBN: 9789811524455$q(electronic bk.)

Standard No.: 10.1007/978-981-15-2445-5doiSubjects--Topical Terms:

210438
Computational biology.

LC Class. No.: QH324.2 / .S738 2020

Dewey Class. No.: 570.285

Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications
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520 $a This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
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