课程名称：M.S in Computational Biology and Bioinformatics
- 授予学位：硕士学位 (Masters)
- 授予机构：Yale University
- 学院：Graduate School of Arts and Sciences, Department of Computational Biology and Bioinformatics
课程介绍：
This program is a rapidly developing multidisciplinary field. The systematic acquisition of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation. Given the rate of data generation, it is well recognized that this gap will not be closed with direct individual experimentation. Computational and theoretical approaches to understanding biological systems provide an essential vehicle to help close this gap. These activities include computational modeling of biological processes, computational management of large-scale projects, database development and data mining, algorithm development and high-performance computing, as well as statistical and mathematical analyses. To qualify for the awarding of the M.S. degree a student must complete two years (four terms) of study in the Ph.D. program, with nine required courses taken at Yale, complete the required course work for the Ph.D. program with an average grade of High Pass, successfully complete three research rotations, and meet the Graduate School’s Honors requirement.
- 国际学生入学要求：
Applicants are expected to have a strong foundation in the basic sciences, such as biology, chemistry, and mathematics, and to have training in computing/informatics, including significant computer programming experience. The Graduate Record Examination (GRE) General Test is required, and the GRE Subject Test in cell and molecular biology, biology, biochemistry, chemistry, computer science, or other relevant discipline is recommended. Alternatively, the Medical College Admission Test (MCAT) may be substituted for the GRE tests. Applicants for whom English is not their native language are required to submit results from the Test of English as a Foreign Language (TOEFL).
The modules include Courses in Computational Biology and Bioinformatics: CBB 752b Bioinformatics Simulation and Data (spring term in 2009-2010), CBB 750a Core Topics in Biomedical Informatics (fall term in 2009-2010), CBB 740a Clinical and Translational Informatics, CBB 645b Statistical Methods in Genetics and Bioinformatics, CHEM 526b Computational Chemistry and Biochemistry, Courses in Biological Sciences: Courses are available in many departments, including Molecular, Cellular, and Developmental Biology, Ecology and Evolutionary Biology, Molecular Biophysics and Biochemistry, Genetics, and Cell Biology. Courses that recent CBB graduate students have taken include the following: CBIO 602a Molecular Cell Biology, GENE 625a Basic Concepts: Genetics Analysis, GENE 777b Mechanisms of Development, IBIO 530a Biology of Immune System, MBB 600a Principles of Biochemistry I, MBB 743b Advanced Eukaryotic Molecular Biology, MCDB 505a Molecular Genetics of Prokaryotes 12, MCDB 570b Biotechnology, PATH 650b Cellular and Molecular Biology of Cancer, EEB 525b Evolutionary Biology, MCDB 561b Systems Modeling in Biology, Informatics Courses: Computer Science and Related Courses: Courses are available in Computer Science and other departments. Example courses that CBB graduate students might take include the following: CPSC 524a Parallel Programming Techniques, CPSC 537a Introduction to Databases, CPSC 545b Data Mining, CPSC 562a Graphs and Networks, CPSC 570a Artificial Intelligence, CPCS 577a Neural Networks for Computing, BIS 560b Database Management in Biomedicine and Epidemiology Statistics Courses, Many CBB students have taken the following statistics courses: STAT 538a Probability and Statistics for Scientists, STAT 645b Statistical Methods in Genetics and Bioinformatics, STAT 660b Multivariate Statistical Methods, CBB students have also enrolled in the following statistics courses: STAT 530b Introductory Data Analysis, STAT 541a Probability Theory, STAT 542b Theory of Statistics, STAT 551b Stochastic Processes, STAT 610a Statistical Inference, STAT 612a Linear Models, STAT 661a Data Analysis, STAT 665b Statistical Machine Learning, BIS 623a Applied Regression Analysis, Research Ethics Course: MB and B 676b Responsible Conduct of Research