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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, and 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.

At the end of the course, students will be able to:

Determine the appropriate method for common data analysis problems.

Assess alternatives if these methods are insufficient.

Interpret the application of commonly used software tools for data modeling, prediction, machine learning, and optimization.

Compose and present a meaningful report of their analysis.

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