Interested in joining our next batch? Applications open soon.

Interested in joining our next batch? Applications open soon.

Degree Level Course

Big Data and Biological Networks

To enable the students to “understand” biological data, to represent, and analyze various datasets from a network perspective, to encourage network thinking applied to problems across disciplines, to understand various network models used to model real-world networks, to apply network analytics techniques to understand biological networks, to implement basic network analysis algorithms in Python, to learn different AI/ML problem formulations for biological data, and to apply AI/ML techniques for analysis of biological data using Python.

Course ID: BSCBT4002

Course Credits: 4

Course Type: Elective

Pre-requisites: None

Course structure & Assessments

12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.

WEEK 1 Introduction to Biological Big Data. Information Flow in Biological Systems.
WEEK 2 Omics datasets: Various flavours of big biological datasets (genomic, transcriptomic, proteomic, metabolomic, etc.).
WEEK 3 Introduction to Graph theory. History. Types of graphs. Representing biological networks.
WEEK 4 Network structure: Key parameters, measures of centrality
WEEK 5 Key Network Models: Erdos-Renyi, Watts-Strogatz (small-world) and Barabasi-Albert (power-law models)
WEEK 6 Network clustering/community detection. Identifying motifs in networks. Studying network perturbations.
WEEK 7 Applications of network biology: Predicting drug targets, predicting drug molecules, synthesis of new molecules (chemoinformatics)
WEEK 8 Applications of network biology: Epidemiology, Centrality-lethality hypothesis.
WEEK 9 AI & ML for Biological Data Analysis. Introduction to AI & ML tasks in biological networks.
WEEK 10 Biological network reconstruction from omics and literature data
WEEK 11 Property prediction using network data. Node classification and link prediction.
WEEK 12 Analysis of heterogeneous and multi-layer/multiplex networks. Future Perspectives.
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