605.754 Analysis of Gene Expression
The rapid popularization of microarray technology has led to an explosion in the collection of gene expression data. After a brief survey of existing gene expression software tools, this course emphasizes the development of original algorithms and data mining techniques for analyzing gene expression data. This course covers statistical and analytical methods and software development for the analysis of gene expression data. Topics include (1) a brief survey of existing software for microarray analysis (normalization, differential expression, and clustering); 2) algorithms, database design and data mining techniques for gene expression data; and 3) detailed coverage of analysis and reverse engineering of gene regulatory networks, including relevance networks, Boolean networks and continuous networks. Both static data and time series data will be considered. The student will develop sufficient expertise in both the underlying analysis and statistical theory to develop new algorithms and software to analyze gene expression data. Students will complete several software assignments including the design of a new algorithm for gene expression analysis. NOTE: There are no exams, but programming assignments are intensive. Students in the MS Bioinformatics program may take both this course and 410.671 Microarrays and Analysis as the content is largely mutually exclusive.
Prerequisites: 605.205 or a course in molecular biology or cell biology, and a course in probability and statistics. Working knowledge of C or JAVA. JHU Online Orientation Course.