IIT Madras BDBN Lectures: Big Data and Biological Networks
BSBT4002105 lectures12 weeks
Weeks
- Week 111 lecturesBig data in biology · Introduction to molecular biology · DNA replication · DNA transcription - part 1 · DNA transcription - part 2 · RNA translation - part 1 · RNA translation - part 2 · Introduction to OMICS · Introduction to genomics epigenomics and transcriptomics · Introduction to proteomics metabolomics and fluxomics · Introduction to multiomics
- Week 27 lecturesGenomics: sanger sequencing · Genomics: illumina sequencing · Genomics: GWAS - part 1 · Genomics: GWAS - part 2 · Genomics: major genome projects · Transcriptomics - part 1 · Transcriptomics - part 2
- Week 310 lecturesIntroduction to networks - part 1 · Introduction to networks - part 2 · Structure of networks: introduction · Computational representation of graphs · NetworkX · Tutorial: introduction to NetworkX - part 1 · Tutorial: introduction to NetworkX - part 2 · Structure of networks: centrality measures · Tutorial: network structure · Network Properties
- Week 49 lecturesNetwork models: random networks · Network models: small world networks · Network models: power law networks · Tutorial: generating network models - part 1 · Tutorial: generating network models - part 2 · Tutorial: generating network models - part 3 · Tutorial: real world networks and Erdos-Renyi networks comparison · Application of network biology: centrality-lethality · Application of network biology: differential network analysis
- Week 59 lecturesCommunity detection · Motifs and network perturbation: introduction · Motif identification · Perturbation to networks · Disease networks and modules · Disease module identification: introduction · Disease module identification: DREAM challenge · Tutorial: motif identification in networks · Tutorial: module detection in networks
- Week 610 lecturesPredicting novel metabolic pathways using molecular graphs and subgraph mining · Graph representation of a molecule · Disease spreading on networks · Reconstruction of biological networks: part 1 · Reconstruction of biological networks: part 2 · Signalling networks: basics · Metabolic networks: basics · Interactions in microbial communities: introduction · Interactions in microbial communities: MetQuest · Interactions in microbial communities: quantification and applications
- Week 76 lecturesGenome assembly: introduction · Genome assembly: overlap graph and the Hamiltonian path problem · De Bruijn graphs: introduction · Assembling genome from an Eulerian graph · Introduction to sequence alignment · The Manhattan tourist problem
- Week 87 lecturesSequence alignment: Needleman-Wunsch algorithm · Sequence alignment: Smith-Waterman algorithm · Tutorial: Eulerian path algorithm · Tutorial: sequence alignment · Graph representation learning: graphs as data · Graph representation learning: learning on graphs · Graph representation learning: embeddings
- Week 97 lecturesNode embeddings: introduction · Node embeddings: encoder-decoder perspective · Node embeddings: factorization based methods - part 1 · Node embeddings: factorization based methods - part 2 · Node embeddings: random walk based methods - part 1 · Node embeddings: random walk based methods - part 2 · Node Embeddings: Shallow Embeddings
- Week 1010 lecturesEdge embeddings: introduction · Edge embeddings: case studies · Deep learning: introduction · Deep learning: artificial neural networks · Deep learning: feed forward example · Deep learning: training neural networks · Deep learning: regularization · Tutorial: node embeddings - part 1 · Tutorial: node embeddings - part 2 · Tutorial: edge embeddings
- Week 119 lecturesGraph neural networks: introduction · Graph neural networks: framework · Graph neural networks: spectral graph filters - part 1 · Graph neural networks: spectral graph filters - part 2 · Graph neural networks: spectral graph filters - part 3 · Graph neural networks: spatial graph filters · Graph neural networks: pooling - part 1 · Graph neural networks: pooling - part 2 · Tutorial: spectral filtering
- Week 127 lecturesGNN: node classification · GNN: graph classification - part 1 · GNN: graph classification - part 2 · GNN: applications to drug discovery - part 1 · GNN: applications to drug discovery - part 2 · GNN: applications to drug discovery - part 3 · Module summary