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Statistical modelling and machine le...

Srinivasa, K. G.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications/ edited by K. G. Srinivasa, G. M. Siddesh, S. R. Manisekhar.
其他作者:	Srinivasa, K. G.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xii, 317 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Computational biology. -
電子資源:	https://doi.org/10.1007/978-981-15-2445-5
ISBN:	9789811524455

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

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

590653
Computational biology.

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

Dewey Class. No.: 570.285

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