Course

Introduction to the Basics of Molecular Biology – DNA,RNA, Genes and Proteins,Central Dogma of Molecular biology, Cell regulation, What is Bioinformatics, Challenges in Bioinformatics – Genome, Transcriptome, Proteome. –Python programming for bioinformatics.

AI in Bioinformatics Applications – Decision Trees, Neural Networks, Application guidelines, Genetic Algorithms, Probabilistic Graphical Models in Bioinformatics, Hybrid Models and Neural Network Parameterization of Graphical Models, Cellular Automata.

Internet Resources and Public Structure Databases in Molecular Biology, Sequences.

Preamble

This course exposes the students to apply AI technology in the domain of Bioinformatics.

Course Objectives

• To incorporate the concepts of bioinformatics using statistics.
• To enhance the application of programming for bioinformatics.
• To explore the challenges in bioinformatics and apply AI for solutions.

Course Outcomes

Prerequisites

• Basic knowledge of Deep Learning
• Python programming

CO-PO Mapping

Evaluation Pattern – 70:30

• Midterm Exam – 30%
• Continuous Evaluation – 40%
• End Semester Exam – 30%

Text Book / References

1. Karthik Raman, an Introduction to Computational Systems Biology (Systems Level Modeling of Cellular Networks), CRC Press, 2021.
2. Bioinformatics algorithm, An active learning Approach’, Phillip Compeauand Pavel PevznerVol.1. and Vol. 2 ,2015.
3. ‘Essential Bioinformatics’, JinXiong, Cambridge University Press, 2006
4. Edward Keedwell and Ajit Narayanan, Intelligent Bioinformatics, John Wiley & Sons Ltd, 2005