MTech Artificial Intelligence & Neural Networks syllabus for 2 Sem 2020 scheme 20BBI253

Introduction to Artificial Intelligence:

Introduction to Artificial Intelligence, Problems, Approaches and tools for Artificial Intelligence. Introduction to search, Search algorithms, Heuristic search methods, Optimal search strategies. Use of graphs in Bioinformatics. Grammers, Languages and Automata. Current Techniques of Artificial Intelligence: Probabilistic approaches: Introduction to probability, Bayes’ theorem, Bayesian networks and Markov networks.

Classification methods:

Nearest Neighbour method, Nearest Neighbour approach for secondary structure protein folding prediction, Clustering and Advanced clustering techniques. Identification Trees - Gain criterion, Over fitting and Pruning. Nearest Neighbour and Clustering Approaches for Bioinformatics.

Applications:

Genetic programming, Neural Networks for the study of Gene-Gene interactions. Artificial neural networks for reducing the dimensionality of expression data. Cancer classification with Microarray data using Support Vector Mechanics. Prototype based recognition of splice sites. Analysis of Large-Scale mRNA expression data sets by genetic algorithms.

Artificial Immune Systems in Bioinformatics

Evolutionary algorithms for the protein folding problem. Considering Stem-Loops as sequence signals for finding Ribosomal RNA genes. Assisting cancer diagnosis. Neural Networks: Methods and Applications. Application of Neural Networks to Bioinformatics. Genetic algorithms and Genetic programming: Single-Objective Genetic algorithm, Multi-Objective Genetic algorithm. Applications of Genetic algorithms to Bioinformatics.

Genetic programming –

Method, Applications, Guidelines and Bioinformatics applications. Boolean Networks, Bayesian Networks and Fuzzy Neural Networks with case studies. Applications of Neural Networks: Introduction, Modeling gene regulatory networks. QSAR and structure prediction with case studies.

Course outcomes:

At the end of the course the student will be able to:

• Understand the concepts of artificial intelligence and their applications in bioinformatics.
• Understand the approaches of neural networks applications of neural networks in biological studies.
• Understand the principals of Genetic programming and Neural Networks

Question paper pattern:

The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60.

• The question paper will have ten full questions carrying equal marks.
• Each full question is for 20 marks.
• There will be two full questions (with a maximum of four sub questions) from each module.
• Each full question will have sub question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each module.

Textbook/ Textbooks

1 Artificial Intelligence Methods and Tools for Systems Biology Werner Dubitzky, Francisco Azuaje, Springer 2005

2 Artificial Neural Networks Yegnanarayana PHI 1998

Reference Books

1 Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems Edward Keedwell, Ajit Narayanan John Wiley and Sons 2005

2 Computational Intelligence in Bioinformatics Arpad Kelemen, Ajith Abraham, Yuehui Chen, SpringerLink Springer 2008

3 (3) Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications Tomasz G. Smolinski, Mariofanna G. Milanova, Aboul Ella Hassanien Springer 2008