Advanced Bioinformatics |
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Time & Place: Instructor: Office Hours and Place:
Class Lecture Notes: Course Description: This is a graduate level course on probabilistic modeling of biological data. The course covers computational approaches to understanding and predicting the structure, function, interactions, and evolution of DNA, RNA, proteins, and related molecules and processes. The emphasis is on providing a unified Bayesian statistical framework to mine large noisy data sets that are becoming the hallmark of modern biology. The methods taught focus on developing the structure of the models, on model fitting algorithms (machine learning), and on the application of the resulting models (data mining). Most applications will revolve around DNA, RNA, protein sequence, and gene-expression-array data, but other types of data will also be considered depending on participants interests.cc Objectives: It provides great learning opportunity for students
who are computer/computational science or engineering major to understand
the needs of advanced probabilistic algorithms in computational biology, especially
in bioinformatics. It also provides potentials for graduate students who are
enthusiastic in learning statistical approaches and algorithms to bioinformatics,
which can be directly used to their research in biological science. Prerequisites: Course Contents:
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