Course Outline
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Feb 15-18) Genome Biology + microarray technology proteomics.
Normalization, estimation of mRNA expression (different technologies) Examples include: SAGE, oligo arrays, cDNA arrays. Quality assessment.
Feb 21-25) Differential Expression - a discussion of some of the, very many, different methods for assessing differential expression.
Biological meta-data: finding and using biological resources such as sequence, GO, KEGG, MIPS, dealing with technical replicates.
Introduction to machine learning for genomic data. Clustering, classification, general principles (knn, kmeans, hierarchical clustering, heatmaps)
April 11-15) Machine Learning for genomic data: algorithms SVM, random forests, feature selection, prediction
Combining data across experiments within species or between types of experiments: microarrays and protein-protein interactions or between species, within technology (microarrays).
May 2-7) Graphs and Networks The basic concepts and uses of graphs and networks. Concepts from Social Network Analysis. Examples of the use of graphs for analysing genomic data.
