INFO-B 528 Computational Analysis of High-throughput Biomedical Data
- Prerequisites: None
- Delivery: On-Campus
This course covers advanced concepts of genomics, molecular biology, and systems biology and explores computational methods for analyzing their high-throughput datasets. Problems in biology and biomedicine will motivate the development of algorithms to apply to these datasets.
- Analyze and process microarray datasets and functionally interpret the results in light of molecular biology.
- Analyze time-course RNA and protein expression levels and model of expression data.
- Analyze genomes comparatively and functionally: a) Predict operon structure, b) Use methods for function prediction, c) Study evolution of operon structure, and d) Apply the principles to metagenomic context.
- Analyze environmental microbial genomic data, resources available for metagenomics, metatranscriptomics, operons and transcription units taxonomic
mapping, microbial abundance, interactions, and pathways.
- Evaluate prediction algorithms and their applications in understanding regulatory systems biology (using representations of regulatory motifs).
- Analyze networks by applying a range of algorithms.
- Evaluate biological networks, by developing and applying computational approaches for analyzing regulatory, protein-protein, genetic, and chromosomal interaction mapping data.
- Evaluate current approaches for determining the structure, dynamics, and evolution of biological networks.
- Write a report and give an oral presentation grounded in an appropriate review of the literature.