Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications
Author(s)
Bibliographic Information
Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications
（Algorithms for intelligent systems）
Springer, c2020
Available at 2 libraries
Note
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
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.
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