Computational Methods in Bioinformatics

Syllabus

Unit I Introduction to Machine Learning

Introduction to machine learning, concepts and definitions, supervised learning, Unsupervised Learning

Unit II Neural Networks (Taught by Mr. B. P. Bag)

Introduction to Neural networks, artificial neural networks, components, models and functions of artificial networks, self-organizational neural networks, fast learning algorithms for neural networks.

Application of neural network in solving biological problems: classification of molecules, prediction of secondary and tertiary structures, molecular evolution, prediction of protein localizing sites, genome mapping

Unit III Genetic Algorithm

Genetic Algorithm Primer, Traditional versus Non - traditional Search, Basic Principles and Features, Encoding Strategy and Population, Evaluation, Genetic Operators – Selection, Crossover, Mutation

Unit IV Probabilistic Approaches

Probabilities and probabilistic models, Conditional, joint and marginal probabilities, Maximum Likelihood estimation, Prior – and posterior probability, comparing models using Bayes Theorem

Papers

  1. Machine Learning and Its Applications to Biology, (2007) Tarca et. al. PloS Computational Biology, Vol 3, Issue 6, e116

Textbooks:

  1. Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications S. Rajasekaran, G.A. Vijayalakshmi Pai
  2. Classification and Learning using Genetic Algorithm,(2007) S. Bandyopadhyay, Sankar K. Pal, Natural Computing Series, Springer
  3. Biological Sequence Analysis (1998) R. Durbin, S. Eddy, A. Krogh und G. Mitchison, Cambridge,

Slides