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