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RBIF 112

Mathematical Modeling for Bioinformatics

The development of new bioinformatics tools typically involves some form of data modeling, prediction or optimization. This course introduces various modeling, prediction, and machine learning techniques including linear and nonlinear regression, principal component analysis, support vector machines, self-organizing maps, neural networks, set enrichment, Bayesian networks, and model-based analysis.

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