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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications|K. G. Srinivasa

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

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Overview

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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Details

  • ISBN-13: 9789811524479
  • ISBN-10: 9811524475
  • Publisher: Springer
  • Publish Date: January 2021
  • Dimensions: 9.21 x 6.14 x 0.69 inches
  • Shipping Weight: 1.03 pounds
  • Page Count: 317

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