Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications (Hardcover)

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.