Bioinformatics Courses at the University of Louisville
Introduction to Bioinformatics |
Covers the current state of the art programs designed for sequence alignment, database searching, RNA structure prediction, microarray, sequence analysis, gene prediction, repeat detection, and protein folding prediction. A detailed analysis of the algorithms behind each of these will be explored. The algorithmic techniques discussed will include dynamic programming, hidden Markov models, finite state automata, grammars, Karlin-Altschul statistics and Bayesian statistics.
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Bioinformatics Journal Club |
Current trends in bioinformatics.
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Mathematical Models in Computational Biology |
Prerequisite: MATH 205 (Calculus I). MATH 561 or equivalent is a plus but not necessary. The course is intended for two types of students: (i) Molecular biology and Biochemistry students interested in learning the mathematical tools of Systems Biology and (ii) Statistics/ Mathematics/Engineering students who would like to learn more about applying probability theory tools to real-life problems in connection with cellular/systems biological models. The objective of the course is to present the basic theory (both from mathematical and biological perspective) behind some of the most popular models employed in the field, as well as give the audience ability to practically utilize that knowledge by mastering the basic software and programming skills needed to perform analysis of the models discussed. These skills shall be build from the ground up so no prior knowledge of computer software or programming is necessary.
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Statistics for Bioinformatics |
Development of high throughput technologies has changed the face of biological sciences. The high dimensional complicated date generated from DNA sequences, amino acid sequences, genetic maps and polymorphic marker data etc. help to unravel the mysteries of many biological processes. However, sophisticated statistical methods and computational tools are needed to analyze these data. This course will introduce basics of genetics and introduction of such data, knowledge of statistical inference and probability, Introduction to stochastic processes, Analysis of DNA and protein sequences, Hidden markov models, Evolutionary models etc.. This course is developed for individuals interested in pursuing research in computational biology, genomics and bioinformatics. Students are expected to be familiar with some elementary statistics and probability concepts.
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High Throughput Data Analysis |
High-throughput technology has changed the dimension of biotechnology. The array of high-speed, highly automated biotechnical equipment DNA sequences, micro array (DNA, Protein), proteomic analyzers (mass spectrometers) and cell sorters are all designed to capture and process vast amounts of biological data at high speeds. WE will briefly discuss some of these technologies. Secondly, this course will concentrate with the process of micro array data mining (analysis) from beginning to end. In particular, this course will provide the researchers and practitioners guidelines to use appropriate statistical methodology for experimental design, image processing, normalization, identifying differentially expressed genes, clustering and classification techniques etc. Introduction to S-PLUS/R library for the data analysis will also be attempted.
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Perl Programming for Biologists |
Informal perl programming for biologists. This course is informally offered once a week, and covers the basics of Perl programming, including loop structures, conditional structures, file input and output, and regular expressions. No prior programming experience is necessary -- the goals of this course are to introduce the basics of perl programming to biologists.
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