This course provides an introduction to statistical
methods in computational and genomic biology.
Statistical topics, to be introduced in a biological
context, include: numerical and graphical summaries of data, basic
notions in probability, loss-based estimation (maximum likelihood,
selection, multiple hypothesis testing, Markov chains, hidden Markov
models, resampling, simulation, introduction to R. Biological questions
to be considered include, but are not limited to: modeling meiosis,
genetic mapping, nucleotide and protein sequence analysis, DNA
microarray data analysis.
The course will also introduce statistical computing resources for the
analysis of biological data, with emphasis on the R language and environment, and Bioconductor software
In addition to discussing specific statistical and computational
methods, the course will provide an introduction to basic notions in
genetics and molecular
biology and will involve the critical reading of articles
related to statistical analyses in the biological and medical sciences.
I encourage you to attend PH
296, the Statistics and Genomics Seminar (schedule