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

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

Good Research Book in Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

Author(s) Name:  K. G. SrinivasaG. M. SiddeshS. R. Manisekhar

About the Book:

   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

  • Introduction to Bioinformatics
  • Review About Bioinformatics, Databases, Sequence Alignment, Docking, and Drug Discovery
  • Machine Learning for Bioinformatics
  • Impact of Machine Learning in Bioinformatics Research
  • Text Mining in Bioinformatics
  • Open-Source Software Tools for Bioinformatics
  • A Study on Protein Structure Prediction
  • Computational Methods Used in Prediction of Protein Structure
  • Computational Methods for Inference of Gene Regulatory Networks from Gene Expression Data
  • Machine-Learning Algorithms for Feature Selection from Gene Expression Data
  • Unsupervised Techniques in Genomics
  • Supervised Techniques in Proteomics
  • Visualizing Codon Usage Within and Across Genomes: Concepts and Tools
  • Single-Cell Multiomics: Dissecting Cancer
  • ISBN:  978-981-15-2445-5

    Publisher:  Springer Publisher

    Year of Publication:  2020

    Book Link:  Home Page Url