Course Description:
This course introduces advanced algorithms for data-intensive computational analysis targeting biological applications such as drug response prediction, gene network analysis, and protein/RNA structure prediction. Main techniques include greedy search, linear regression, clustering, network analysis, expectation maximization, and Hidden Markov models, which are widely applicable beyond biological data.Prerequisite:
CS 5329 or CS 5369L or equivalent with a grade of B or higher, or consent of the instructor.
Course Objectives:
The students will be able to:
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Recognize the computational challenges in analyzing biological data
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Relate those challenges with the standard machine learning algorithms
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Formulate biological questions mathematically
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Develop and customize novel algorithms to analyze data produced by new technology
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Measure the accuracy of approaches and compare different techniques
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Recognize the limitations of the statistical models in high dimensional settings
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List publically available biological and clinical datasets and employ them to improve the analysis when the available data are limited
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Combine different bioinformatics tools to answer specific biological questions
Course Notes:
New course effective Fall 2017. Available only for computer science majors.