INFO-B 528 Computational Analysis of High-throughput Biomedical Data
3 credits
- Prerequisites: None
- Delivery: On-Campus
- 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.
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.