NUS Computing, School of Computing. The Department of Computer Science. Database Management, Media, Systems and Networking, Computational Biology. Undergraduates Programmes in NUS. Next Generation DNA Sequencing IPM-NUS Workshop on Computational Biology Mehdi Sadeghi. 147 Repeats Repeats in the sequence –Assembly programs should. Degree & Certificate Programs in Bioinformatics Program Goals: This programme aims to provide a multidisciplinary education that would produce graduates who would be equally at ease with algorithm design, mathematical and statistical analysis as they would be with biochemistry, biology/genetics, and wet- lab know- how. Graduates from the programme will be equipped for a career in the fast- paced pharmaceutical, biomedical or biotechnology industries. They could also pursue graduate studies in bioinformatics.
Bioinformatics- related courses in NUS CS2. Introduction to Computational Biology. Instructor: Wong Limsoon /. Course Homepage /. The goals of CS2. Introduction to Computational Biology) are. In the post- genome era. This module is intended. SNPs, mass spectrometry, etc. This aim of this module is to cover the algorithms. It studies exact. This module is for students with. Coupled with the advances in computing power. Handling them using brute- force approaches. This module is. an in- depth study of some of these advance algorithms. Through the course. They are also. given a chance to solve some research problems in this field, including. Thiagarajan /. Course Homepage /. This course provides an introduction to modeling and analysis techniques. We shall introduce models such as ordinary differential equations. Petri nets, Markov chains and dynamic Bayesian networks and show how. Selfstudy, tool- based modeling assignments and. Techniques for genetic analysis and the use of model. Escherichia coli, Drosophila and higher plants. Commonly used data structures. AVL trees), hashing, tables, and graphs; together with. Simple algorithmic paradigms,such as. This module is targeted at students who are. Statistics and are able to meet the pre- requisites. Students will learn about the framework for algorithm analysis. NP- completeness. In addition, students are exposed to. The module serves two purposes. Fundamental algorithmic solving techniques covered include. Domain specific techniques like number theory, computational. The module also covers algorithmic. The main reason for studying computational learning. The ultimate objective is to build. At the end of the course, students are expected to. Topics covered include: normalisation theory. The aim of this module is to prepare students. The module starts with motivations, background and. The main content has five parts. Descriptive statistics. Efforts have been directed at. ML) techniques for decision making and. ML techniques. such as artificial neural network methods have been proven to be. Applications include credit rating. The main focus will be on probabilistic models. Bayesian networks and Markov networks. Topics include. representing conditional independence, building graphical models.
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