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Education

Capstone Certificate in Bioinformatics
Course Sequences and Descriptions

The Capstone Certificate in Bioinformatics consists of four postdoctoral courses for a total of 12 semester credits. The program typically can be completed in 1-2 years. Three of the courses are required, while one is an elective:


Basic Course Requirements Elective Courses
Choose one of two Statistics courses Both of these courses are required Choose one elective

 


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** Independent study in another department may be substituted with prior approval.

Statistics courses (one is required)

Biostatistics and Medical Informatics 541 (3 credits) - Introduction to Biostatistics - is designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications are discussed for each topic. Prerequisites: Math 221 or equivalent or instructor's consent.

Statistics 571 (4 credits) - Statistical Methods for Bioscience I - is a design course aimed at CALS graduate students but the principles are quite applicable to molecular biology. Topics include: Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way ANOVA, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work.

Biostatistics and Medical Informatics courses (both are required)

Biostatistics and Medical Informatics 576 (3 credits, cross-listed as Computer Sciences 576) - Introduction to Bioinformatics - The goals of this course are to provide an understanding of the fundamental computational problems in molecular biology and a core set of widely used algorithms. This is the first of two courses on bioinformatics. The topics it will cover include: pairwise sequence alignment, multiple sequence alignment, finding genes in DNA sequences, phylogenetic tree construction, and genome mapping and sequencing. This is currently being taught as a special topics course in Computer Sciences. Prerequisites: Math 222 and Computer Sciences 367.

Biostatistics and Medical Informatics 776 (3 credits, cross-listed as Computer Sciences 776) - Advanced Bioinformatics - The goals of this course are to provide an understanding of the fundamental computational problems in molecular biology and a core set of widely used algorithms. This is the second of two courses on bioinformatics. The topics it will cover include: probabilistic methods for sequence modeling, gene expression analysis, phylogenetic tree construction, protein structure prediction, RNA modeling, whole-genome analysis, and algorithms for exploiting biomedical text sources. Prerequisites: Biostatistics and Medical Informatics 576.

Elective Courses (choose one) - Additional elective courses are expected to be added as faculty are recruited.

Biochemistry 711/712 (3 credits for 711, 1 credit for 712) - Sequence Analysis - This is a two-part course beginning with a lecture/discussion group course (711) and finishing with a hands-on laboratory course, taught at actual computer terminals, designed to complement and reinforce the sequence analysis concepts presented in Biochemistry 711. This course give students a practical background in using many available software packages such as DNA-STAR for gene sequencing, etc. Students are provided with actual data and gain experience using this software. Prerequisites: Graduate level standing.

Biostatistics and Medical Informatics 542 (3 credits) - Fundamentals of Clinical Trials- Intended for biomedical researchers interested in the design and analysis of clinical trials. Topics include definition of hypotheses, measures of effectiveness, sample size, randomization, data collection and monitoring, and issues in statistical analysis. Prerequisites: Statistics 541 or equivalent or instructor's consent.

Bioinformatics Independent Study 799 (3 credits)- Some students may find their needs are better met by an independent study with one of the faculty in the department, in collaboration with a biological faculty member. Independent study in another department may be substituted with prior approval.

Computer Science 540 (3-4 credits) - Introduction to Artificial Intelligence - Teaches principles of knowledge-based search techniques; automatic deduction; knowledge representation using predicate logic, semantic networks, connectionist networks, rules, machine learning, applications in problem solving, expert systems, game playing, natural language understanding. Prerequisites: Computer Sciences 367.

Computer Science 545 (3 credits) - Natural Language and the Computer - The course covers basic techniques and tools in natural language processing: generative grammars, parsing, dictionary construction, semantic networks, generation of text from a knowledge base, natural language interfaces, and machine translation. Prerequisites: CS 536 or CS 537 or 564 or consent of instructor.

Computer Science 564 (3 credits) - Database Management Systems: Design and Implementation - What a database management system is; different data models currently used to structure the logical view of the database: relational, hierarchical, and network. Hands-on experience with relational and network-based database systems. Implementation techniques for database systems. File organization, query processing, concurrency control, rollback and recovery, integrity and consistency, and view implementation. Prerequisites: CS 367 and 354.

Computer Science 577 (3 credits) - Introduction to Algorithms - Survey of important and useful algorithms for sorting, searching, pattern-matching, graph manipulation, geometry, and cryptography. Paradigms for algorithm design. Techniques for efficient implementation. Prerequisites: CS 367, and CS 240, or consent of instructor.

Computer Science 731 (3 credits) - Advanced Artificial Intelligence - Novel techniques within Bayesian Networks, Machine Learning and Data Mining, Planning and Computer Vision have proven useful for many real-world problems. This course will cover some of the most important recent algorithms from these areas and will illustrate their use with biomedical applications. Prerequisites: Computer Sciences 540.

Computer Science 760 (3 credits) - Machine Learning - The intent of this course is to present a broad introduction to machine learning, including discussions of each of the major approaches currently being investigated. Class lectures will discuss general issues in machine learning, as well as present established algorithms. Computational approaches to learning, including: inductive inference, explanation-based learning, analogical learning, connectionism, and formal models, what it means to learn, algorithms for learning, comparison and evaluation of learning algorithms, cognitive modeling and relevant psychological results. Prerequisites: Computer Sciences 540.

Computer Science 766 (3 credits) - Computer Vision - an introductory course to the basic concepts in computer vision including fundamentals of image analysis and computer vision, image acquisition and geometry, image enhancement, recovery of physical scene characteristics, shape-form techniques, segmentation and perceptual organization, representation and description of two-dimensional objects, shape analysis, texture analysis, goal-directed and model-based systems, parallel algorithms and special purpose architectures. Prerequisites: Computer Sciences 540.

Industrial Engineering 617 (3 credits) - Health Information Systems (previously offered as IE 691) - Introduction to health information systems and health informatics. Major topics include clinical information systems, formal language and vocabularies, telemedicine, image technology and public health informatics. Lectures by local and national experts will be followed by instructor-facilitated discussion examining how industrial engineering tools and perspectives could improve the quality, efficiency and effectiveness of health information. Prerequisites: Senior or Graduate Standing, or instructor's consent.

Mathematics 605 (3 credits) - Stochastic Methods for Biology - This course is intended to provide a rigorous foundation for stochastic modeling of biological systems. The mathematical emphasis is in stochastic analysis and simulation. Biological applications include epidemiological phenomena, biochemical reaction networks and population dynamics. Prerequisites: Math/Stat 431, Math/Stat 309 or Stat 311, or consent of instructor.

Mathematics 606 (3 credits) - Mathematical Methods for Structural Biology - This course will provide a rigorous foundation for mathematical modeling of biological structures. Mathematical techniques include ordinary and partial differential equations, 3D Fourier analysis and optimization. Biological applications include protein folding, molecular dynamics, implicit solvent electrostatics, and molecular interactions. Prerequisites: Math 340 or 341, CS 302, or consent of instructor.

Mathematics 608 (3 credits) - Mathematical Methods for Continuum Modeling in Biology - This course is intended to provide a rigorous foundation for mathematical modeling of biological systems. The mathematical emphasis is on partial differential equations, particularly reaction-diffusion and transport equations. Biological applications include bacterial chemotaxis, spatio-temporal ecological dynamics, and cell-level reactions. Prerequisites: Math 322, Math 415, Math 514, or consent of instructor.

Mathematics 609 (3 credits) - Mathematical Methods for Systems Biology - This course is intended to provide a rigorous foundation for mathematical modeling of biological systems. Mathematical techniques include dynamical systems and differential equations. Applications to biological pathways, including understanding of bistability within chemical reaction systems, are emphasized. Prerequisites: Math 340 or 341, Math 415 or consent of instructor.

   

 
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