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AKTU Machine Learning in Bioinformatics (HTCB501) Syllabus PDF – B.Tech Honors Degree in Computational Biology and Bioinformatics
Machine Learning in Bioinformatics HTCB501
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Official AKTU syllabus of Machine Learning in Bioinformatics (HTCB501) for B.Tech Honors Degree in Computational Biology and Bioinformatics – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Machine Learning in Bioinformatics
Download PDFHTCB501 · 4 credits · L-T-P 3-1-0
Course Outcomes
| CO1 | Understand various protein structure prediction methods, next generation sequencing, microarray data analysis and relation methods to understand the application of machine learning methods. |
| CO2 | Understand different types of machine learning methods and their utility in bioinformatics. |
| CO3 | Apply basic concepts of molecular modelling and simulation. |
| CO4 | Explore concepts behind document data management, document clustering and natural language |
processing.
| CO5 | Implement different methods of drug designing and use of various machine learning tools. |
Detailed Syllabus (Theory)
| Unit | Topics | CO | No. of proposed lectures |
|---|---|---|---|
| I | Protein Structure prediction (Secondary and Tertiary), Genomic sequence annotation, Protein function prediction, Next generation sequencing, Gene expression analysis, Microarray data analysis, Data mining, Biological network identification and gene regulation. | 1 | 8 |
| II | Machine learning – concepts of supervised and unsupervised learning, Classification: Decision tree induction, Artificial Neural Networks, Hidden Markov Models, Genetic Algorithms, Support vector machines; Clustering: Hierarchical and K-mean; The relation between statistics and machine learning; Evaluation and validation methods. | 2 | 8 |
| III | Molecular Modeling, Basic concept of Force field; Simulation: concepts and computer simulation techniques, types of computer simulation (Continuous, Discrete-event and Hybrid simulation). | 3 | 8 |
| IV | Overview of key techniques for the management of large document collections and the biological literature: Document clustering, Information retrieval system; Natural Language Processing: Introduction, Major areas of NLP, Natural language information extraction. | 4 | 8 |
| V | Computer aided drug designing – concepts, major steps in Drug Designing, Ligand and Structure based drug designing, Lipinski’s rule of five, Protein ligand docking, QSAR Modeling, Pharmacodynamics, Pharmacokinetics & Pharmacogenomics. | 5 | 8 |
Text Books
- Arthur M.Lesk, Introduction to bioinformatics, Oxford University Press.
- S.C. Rastogi & others, Bioinformatics- Concepts, Skills, and Applications, CBS Publishing.
- R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Biological Sequence Analysis:Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press
- P Baldiand S Brunak, BIOINFORMATICS: The Machine Learning Approach
Reference Books
- Olson et al., 2018. Data-driven advice for applying machine learning to bioinformatics problems
- Stephen Misner& Stephen Kraeetz; Bioinformatics- Methods and protocols, Humana press. 3.Andreas D Baxevanis& B F Francis, Bioinformatics- A practical guide to analysis of Genes & Proteins, John Wiley.
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