This course focuses on understanding, representing, and analyzing biological data from a network perspective, including network models, analytics techniques, and basic algorithm implementation in Python. It also introduces AI/ML formulations and their application to biological data analysis.

Lectures-Tutorials-Practicals-Credits: 4-0-0-4

Week 1: Big data in biology; Molecular biology basics; DNA and RNA processes; OMICS and multiomics.

Week 2: Genomics sequencing; GWAS; Genome projects; Transcriptomics.

Week 3: Network fundamentals; Graph representation; Network properties; Centrality; NetworkX.

Week 4: Network models; Model generation; Real-world vs random networks; Network biology applications.

Week 5: Community detection; Network motifs and perturbations; Disease networks; Module identification; Tutorials.

Week 6: Metabolic pathways and graph mining; Molecular graphs; Disease spread; Network reconstruction; Biological and microbial networks.

Week 7: Genome assembly; Overlap and De Bruijn graphs; Sequence alignment; Manhattan tourist problem.

Week 8: Sequence alignment; Eulerian paths; Graph representation learning; Graph embeddings.

Week 9: Node embeddings; Encoder-decoder view; Factorization methods; Random walk methods.

Week 10: Edge embeddings; Deep learning basics; Neural network training; Regularization; Embedding tutorials.

Week 11: Graph neural networks; Spectral and spatial filters; Pooling; Spectral filtering tutorial.

Week 12: GNN tasks; Graph classification; Drug discovery applications; Summary and tutorial.