Teaching

 

Spring 2012: Translational Bioinformatics

3 credit, Wednesdays 9-noon, VALE M-184 (200 Meyran Avenue, Mezannine floor

The field of Translational Bioinformatics emerged because of the recent advances in biotechnology with which several new types of patient and disease specific data are being created for large subpopulations. Computer science methods are being rapidly adapted to process and analyze this data, with the goal of drawing biologically and medically-relevant inferences. By analyzing these different types of data individually or integratively, it is now feasible to attempt deciphering biological root cause of a disease (at least the .why. of the disease if not .how.), to identify biomarkers, and to design personalized medicine. Course goals: Gain familiarity of data produced with current biotechnologies, such as DNA arrays (e.g. SNP data), microarrays (transcriptional profiles), proteomics (mass spectrometry data), epigenomics (methylation profiles). Understand what can be done with such data to infer relations between genome óigenome ód phenome, in order to discover molecular mechanisms of diseases, or identify biomarkers, or discover novel therapies for diseases.

Spring:

Algorithms for Computational & Predictive Biomedicine
3 credit, TTh 10-1130, VALE M-184

This course teaches widely-used computational approaches from disparate fields, specificially, machine learning, signal and image processing, natural language processing and graph theory. Each algorithm will be presented with application to a specific problem in the area of computational biomedicine or predictive medicine. By presenting the most fundamental concepts or algorithms from each of these fields, this course provides the students with the ability to identify the best algorithm or the field of approach to solve a biomedical question at their hand. 

Prerequisites: Working knowledge of Calculus, Probability Theory and Linear Algebra
Course goals: