Bioinformatics Course Offerings
Compiled by Milosavljevic, Aleks, Ph.D., June 5, 2011
This course is intended to provide a background in the theory and application of standard computational methods for molecular biology research. The topics to be discussed include databases, sequence comparison, phylogeny, pattern inference and matching, RNA secondary structure, and protein structure. The course will also address computational issues for the Human Genome Program in the areas of large-scale DNA sequencing, chromosome mapping, and gene recognition. During the term, a seminar speaker, with expertise in an area relevant to the subject area of the course, is invited as a guest lecturer. Students are required to attend this seminar. Credits: 3 Term: 4 Director: Dr. Kim Worley
This course will cover basic concepts for statistical analysis of quantitative data. The focus will be on applying computer-assisted statistical analyses of data commonly obtained in biomedical research. Students will learn how to characterize data, select appropriate statistical tests for analyses, and properly interpret statistical results for testing research hypotheses using statistical software. Credits: 3 Term: 4 Director: Dr. Richard Paylor
An introductory course in the design of experiments and the analysis of experimental data. Actual experiments and the statistical evaluation of the results are utilized to illustrate statistical principles. Credits: 3 Term: 5 Director: Dr. Egon Durban
This course will introduce biostatistical principles and technology most likely to be useful to laboratory scientists interested in translational research, including ANOVA, linear regression, logistic regression, survival analysis, and nonparametric statistics. The course also introduces basic designs for clinical trials and statistical analysis of genomic data from clinical samples. Credits: 3 Term: 4 Director: Dr. Susan Hilsenbeck
This course emphasizes the basis principles of Mendelian inheritance; dominance and recessivity; gene, gene product, and phenotype relationships; allelic and nonallelic heterogeneity; molecular analysis of mutations, monogenic, multifactorial, and environmental interactions; linkage disequilibrium; haplotype analysis; selective advantage; biochemical and molecular diagnosis; heterozygote and newborn screening methods; and pathogenesis. In addition to cytogenetic mechanism of human reproduction and disease, novel mechanisms involving genomic imprinting, uniparental isomy and mitochondrial inherited disorders will be reviewed. The principles are taught by a systematic review of human disorders including lipoprotein disorders, disorders of metal and heme metabolism, disorders of transport, transcription factor disorders, muscular diseases, connective tissue diseases, skeletal dysplasias, and cytogenetic abnormalities. Credits: Term: 5 Director: Dr. Suzanne Leal
Introduce essential computational and mathematical concepts to students who are interested in computational biology and bioinformatics. It is intended that each of the concepts will be taught in the context of the real biological problems ranging from genomics to structural biophysics. Credits: 4 Term: 2 Director: Dr. Wah Chiu
This course is designed for students n computationally-oriented theoretical, biophysical, biomedical and bioengineering majors to introduce the principles and methods used for computer simulations and modeling of macromolecules of biological interest. Fundamental concepts in statistical mechanics, thermodynamics, and dynamics will be emphasized. Protein conformation/dynamics, empirical energy functions and molecular dynamics calculations, as well as other approaches will be described. Specific biological problems are discussed to illustrate the methodology. Classic examples such as the cooperative mechanisms of hemoglobin and more frontier topics such as the motional properties of molecular motors and ion channels as well as results derived from the current literature are covered. Other potential topics ar eprotein folding/predictions, the nature of recaction rate enhancement in enzume catalysis, physical chemistry properties of biologically relevant nano-materials, simulations of free energy changes in mutations, electrostatic properties of protein, molecular recognition, and the properties of binding sites. Particular emphasis is also given to the applications of molecular graphics. During the final reading period, each student carrier out an original research project that makes use of the techniques and grading is based on the written and oral presentations of the results from the final projects. Credits: 6 Term: 1 Director: Dr. Jianpeng Ma
This course will provide scientists with basic programming skills for simple tasks, and to make better use of the programmability available in many modern applications. For example, a scientific visualization package may have a programming interface that allows users to build animation scripts, image processing packages have interfaces that permit analytical measurements or complex batch processing on sets of images, etc. Students will learn Python and Ruby, the two most common scripting languages in scientific programming, and be introduced to other languages such as Perl, C++ and Java. These languages will run on virtually any computer, are freely available, and interface with many different scientific software packages. This course is aimed at students ranging from those with absolutely no programming background to those who have rudimentary programming skills. Some basic familiarity with using a computer will be expected. The course will be taught by Dr. Ludtke (Python/structural biology) and Andrew Jackson (Ruby/genetics). Access to a computer, and preferably a laptop, is required for this class. Credits: 3 Term: 4 Directors: Dr. Steve Ludtke
The objective of this course is to introduce students to the concepts, methods and tools relevant for computer-aided discovery using data collected using high-throughput technologies. The course will focus on the methods of integration of data, tools, and discovery processes and the methods of computational pattern discovery, hypothesis generation and testing. The students will master advanced applications of computing that enable new methods of discovery in a field of focus, which will initially be cancer biology. The course will not focus exclusively on technical, algorithmic or mathematical aspects nor will it focus on biology alone. Instead, the focus will be on genuine integration of the two fields. Credits: 3 Term: 3 Director: Dr. Aleksander Milosavljevic
BIOC 533 - BIOINFORMATICS & COMPUTATIONAL BIOLOGY Credits:2
An introduction to the emerging field of bioinformatics. A series of lectures, combined with hands-on exercises. The topics to be discussed include sequence comparison, structure analysis, phylogenetics, database searching, microarrays and proteomics. Recommended prerequisite(s): BIOC 301 (formerly BIOS 301) or permission of instructor. College: School of Natural Sciences Department: Biochemistry & Cell Biology
BIOC 571 - BIOINFORMATICS: SEQUENCE ANALYSIS Credits: 3
Pairwise and multiple sequence alignment, Markov chains and HMMs, Phylogenetic reconstruction, Haplotype inference, Computational models of RNA structure, Gene finding, Genome rearrangements, and comparative genomics. Cross-list: COMP 571. College: School of Natural Sciences Department: Biochemistry & Cell Biology
BIOC 572 - BIOINFORMATICS: NETWORK ANALYSIS Credits: 3
This course covers computational aspects of biological network analysis, a major theme in the area of systems biology. The course addresses protein-protein interaction networks, signaling, and metabolic networks, and covers issues related to reconstructing, analyzing, and integrating various types of networks. Cross-list: BIOE 564, COMP 572. College: School of Natural Sciences Department: Biochemistry & Cell Biology
This is a course designed for students in computationally-oriented biomedical and bioengineering majors to introduce the principles and methods used for the simulations and modeling of macromolecules of biological interest. Protein conformation and dynamics are emphasized. Empirical energy function and molecular dynamics calculations, as well as other approaches, are described. Specific biological problems are discussed to illustrate the methodology. Cross-list: BIOE 589. College: School of Natural Sciences Department: Biochemistry & Cell Biology
A discussion of selected research topics in computational biology. Cross-list: KECK 592. College: School of Natural Sciences Department: Biochemistry & Cell Biology
This course offers a hands-on application to systems biology modeling. Students will learn a range of modeling methods, and apply them directly in class to current bioengineering problems. Weekly tutorials will be offered, and a laptop is required (or can be loaned). Topics covered include in silico drug delivery and design studies, integrating multiscale models with high-resolution imaging, experimental design vial computer modeling, and patient-specific simulations. Modeling methods include protein-protein interaction networks, biocircuits, stochastic differential equations, agent-based modeling, computational fluid dynamics, and finite element modeling. College: School of Engineering Department: Bioengineering Pre-requisites: BIOE 391
Fundamental concepts of finite element methods in fluid mechanics, including spatial discretization and numerical integration in multidimensions, time-integration, and solution of nonlinear ordinary differential equation systems. Advanced numerical stabilization techniques designed for fluid mechanics problems. Strategies for solution of complex, real-world problems. Topics in large-scale computing, parallel processing, and visualization. Cross-list: CEVE 454, MECH 454, Graduate/Undergraduate Equivalency: BIOE 554. College: School of Engineering Department: Bioengineering
This course portends to give a balanced view of current developments in integrative biology that may lead to future design concepts for the molecular therapy of malignancy. College: School of Engineering Department: Bioengineering
Contemporary introduction to problems in computational biology spanning sequence to structure. The course has three modules: the first introduces students to the design and statistical analysis of gene expression studies; the second covers statistical machine learning techniques for understanding experimental data generated in computational biology; and the third introduces problems in the modeling of protein structure using computational methods from robotics. The course is project oriented with an emphasis on computation and problem-solving. Cross-list: COMP 470, STAT 470. Recommended prerequisites: COMP 280 and (STAT 310 or STAT 331). College: School of Engineering Department: Bioengineering
BIOE 552 - INTRODUCTORY COMPUTATIONAL SYSTEMS BIOLOGY: MODELING & DESIGN PRIN OF BIOCHEM NETWORKS Credits: 3
The course summarizes techniques for quantitative analysis and simulations of basic circuits in genetic regulation, signal transduction and metabolism. We discuss engineering approaches adapted to computational systems biology and aim to formulate evolutionary design principles explaining organization of networks in terms of their physiological demands. We discuss biochemical simulation methodology and software as well as recent advances in the field. Topics include end-product inhibition in biosynthesis, optimality and robustness of the signaling networks and kinetic proofreading. Students are expected to represent several journal articles. Same as 490 but with more emphasis on recent advances in the field - paper reading and presentations. Graduate/Undergraduate Equivalency: BIOE 490. Basic knowledge of biochemistry, cell biology, linear algebra, and ordinary differential equations is expected. College: School of Engineering Department: Bioengineering Pre-requisites: (MATH 212 OR MATH 213) AND BIOS 314
Advanced topics in computational mechanics with emphasis on finite element methods and fluid mechanics. Stabilized formulations. Fluid-particle and fluid-structure interactions and free-surface and two-fluid flows. Interface tracking and interface-capturing techniques, space-time formulations, and mesh update methods. Enhanced discretization and solution techniques. Itertive solution methods, matrix-free computations, and advanced preconditioning techniques. Cross-list: CEVE 654, MECH 654. College: School of Engineering Department: Bioengineering Pre-requisites: BIOE 554 OR CEVE 554 OR MECH 554 OR BIOE 454 OR CEVE 454 OR MECH 454 or permission of instructor
An introductory course in numerical analysis with computer applications. Topics include floating point arithmetic; algorithms for the solution of linear systems, linear least square problems, and nonlinear equations; interpolation; Fourier transform; numerical integration; numerical solution of ordinary differential equations. Computer programming in Matlab is required. Recommended Prerequisites: (MATH 212 or MATH 222) AND CAAM 335. College: School of Engineering Department: Comp. & Applied Mathematics
Scientific programming using high level languages, including C, Fortran, and C++. Emphasis on use of numerical libraries. Basic techniques of project planning, source management, documentation, program construction, i/o, visualization. Object-oriented design for numerical computing. Recommended Prerequisite(s): (CAAM 210 AND CAAM 335) OR CAAM 353. College: School of Engineering Department: Comp. & Applied Mathematics
Vector, shared-memory, and message-passing parallel computer architectures. Numerical linear algebra for these architectures. Memory hierarchy issues, analysis and enhancement of performance, and use of programming tools and environments. Application interfaces including OpenMP and MPI, parallel numerical algorithms and scientific visualization. Recommended Prerequisite(s): CAAM 420. College: School of Engineering Department: Comp. & Applied Mathematics
The ability to sequence whole-genomes of organisms has ushered in a new era in which biology has become an information science. This era has also given rise to the interdisciplinary area of computational biology. This course aims to introduce students to this challenging, yet exciting area by emphasizing the central role of computer science in formulating biological problems, generating hypotheses, and making discoveries. College: School of Engineering Department: Computer Science
Large accessible data sets have opened new frontiers in evolutionary biology, and many fields. Learn to write computer programs to test hypotheses and discover patterns in diverse data. Understand the most common strategies in evolutionary bioinformatics, including dynamic programming, hidden Markov models, and graphical algorithms. No previous programming experience required. Biosciences Group B. Cross-list: EBIO 333. Recommended Prerequisite(s): MATH 101 and MATH 102. College: School of Engineering Department: Computer Science
Contemporary introduction to problems in computational biology spanning sequence to structure. The course has three modules: the first introduces students to the design and statistical analysis of gene expression studies; the second covers statistical machine learning techniques for understanding experimental data generated in computational biology; and the third introduces problems in the modeling of protein structure using computational methods from robotics. The course is project oriented with an emphasis on computation and problem-solving. Cross-list: BIOE 470, STAT 470. Recommended Prerequisites: COMP 280 and (STAT 310 or STAT 331). College: School of Engineering Department: Computer Science
This course provides an overview of topics, concepts, theories and methods that form the foundations of health information sciences. It gives students the fundamental knowledge and skills to pursue further study in health informatics. KECK 501 is taught every semester as a web-based course. Department permission required College: School of Natural Sciences Department: Keck Center
This course provides an overview of theories and methods that are broadly applicable to all health informaticians. It gives students the theoretical and methodological background needed to pursue study in health informatics. The course begins with theories of information from computational, philosophical, mathematical, logical, and biomedical perspectives. Department permission required College: School of Natural Sciences Department: Keck Center Pre-requisites: KECK 501
A discussion of selected research topics in computational biology. Cross-list: BIOC 592. College: School of Natural Sciences Department: Keck Center
NSCI 230 - COMPUTATION IN SCIENCE AND ENGINEERING Credits: 3
The course introduces basic techniques for problem solving and visualization using computational environments such as Mathematica and MATLAB. Class will consist of a mixture of traditional lectures held in classrooms and self-paced modules covering topics in science and engineering that will be completed in Symonds II. No previous experience is required or expected. Cross-list: COMP 110. College: School of Natural Sciences Department: Natural Sciences
The formation of models of reality and the ways models are tested by their analysis in the light of data are considered. We cover a variety of examples from antiquity to the present time. College: School of Engineering Department: Statistics
STAT 280 - ELEMENTARY APPLIED STATISTICS Credits: 4 Course
Topics include basic probability, descriptive statistics, probability distributions, confidence intervals, significance testing, simple linear regression and correlation, association between categorized variables. College: School of Engineering Department: Statistics
Examples to illustrate mathematical and statistical formulation (modeling) of scientific problems, their solution and interpretation. Problems from engineering, epidemiology, economics, and other areas are covered. Real-world situations are emphasized. Satisfies statistics design criteria. College: School of Engineering Department: Statistics Pre-requisites: MATH 211
An introduction to statistics for Biosciences with emphasis on statistical models and data analysis techniques. Computer-assisted data analysis, examples, is explored in laboratory sessions. Topics include descriptive statistics, correlation and regression, categorical data analysis, statistical inference through confidence intervals and significance testing, rates, and proportions, basic epidemiology. Real-world examples are emphasized; for example, genetics, dose-response, biological assays. College: School of Engineering Department: Statistics Pre-requisites: MATH 101 AND MATH 102
Probability and the central concepts and methods of statistics including probability distributions, expectation, estimation, hypothesis testing, sampling distributions, linear models. Section 1 presents the general use in multiple disciplines; section 2 focuses on problem sets and examples in civil and environmental engineering. Cross-list: ECON 307. Recommended prerequisite(s): MATH 212. College: School of Engineering Department: Statistics Pre-requisites: MATH 102
Elementary probability theory, conditional probability, independence, discrete and continuous random variables, expectation, standard discrete and continuous distributions, transformation techniques, central limit theorems, estimation, and correlation. Selected topics such as the Poisson process, Markov chains, and statistical techniques. Illustrations from engineering are emphasized. Cross-list: ELEC 331. College: School of Engineering Department: Statistics
This one hour course covers application and development of methods in statistical inference. The course is designed to cover the inference material from STAT 310 that is not offered through STAT 331 or STAT 312. Students taking STAT 331 or STAT 312 and STAT 340 will meet the prerequisite requirement of STAT 310. If students do not have STAT 312, 331, or ELEC 303 in their academic history, the system will allow registration of the missing prerequisite(s) the same semester as STAT 340. College: School of Engineering Department: Statistics Pre-requisites: STAT 331 OR STAT 312 OR ELEC 303
The three general topic areas covered in this methodology oriented course are statistical methods including regression, sampling, and experimental design; simulation based methods in statistics, queuing and inventory problems; and an introduction to optimization methods. Excel will serve as the basic computing software. College: School of Engineering Department: Statistics Pre-requisites: STAT 280 OR STAT 305 OR STAT 310 OR ECON 382 OR (STAT 312 AND STAT 340) OR (STAT 331 AND STAT 340)
Programming techniques and tools useful in advanced statistical studies. Higher level graphical methods and exploratory data analysis. College: School of Engineering Department: Statistics
A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single- and multi-factor studies, analysis of variance, analysis of covariance, model selection, diagnostics. Data analysis using statistical software is emphasized. College: School of Engineering Department: Statistics Pre-requisites: STAT 310
Advanced topics in statistical applications such as sampling, experimental design and statistical process control. College: School of Engineering Department: Statistics Pre-requisites: (STAT 310 OR ECON 382) OR (STAT 312 AND STAT 340) OR (STAT 331 AND STAT 340)
This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Computational methods will be emphasized. Graduate/Undergraduate Equivalency: STAT 622. College: School of Engineering Department: Statistics
Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution. Graduate/Undergraduate Equivalency: STAT 623. College: School of Engineering Department: Statistics
Topics include random variables, distributions, transformations, moment generating functions, common families of distributions, independence, sampling distributions, the basics of estimation theory, hypothesis testing and Bayesian inference. College: School of Engineering Department: Statistics
An overview of statistical methodologies useful in the practice of Biostatistics. Topics include epidemiology, rates, and proportions, categorical data analysis, regression, and logistic regression, retrospective studies, case-control studies, survival analysis. Real biomedical applications serve as context for evaluating assumptions of statistical methods and models. S-Plus (R) serves as computing software. Graduate/Undergraduate Equivalency: STAT 553. College: School of Engineering Department: Statistics Pre-requisites: STAT 410 or permission of instructor
Modern Topics in Bayesian Statistics. College: School of Engineering Department: Statistics Pre-requisites: STAT 422 OR STAT 622
STAT 532 - MATHEMATICAL STATISTICS I
The first semester in a two-semester sequence in mathematical statistics: random variables, distributions, small and large sample theorems of decision theory and Bayesian methods, hypothesis testing, point estimation, and confidence intervals; topics such as exponential families, univariate and multivariate linear models, and nonparametric inference will also be discussed. Required for graduate students in statistics. College: School of Engineering Department: Statistics Pre-requisites: STAT 410 AND STAT 431 or permission of instructor
A continuation of STAT 532. Required for Ph.D. students in statistics. College: School of Engineering Department: Statistics Co-requisites: STAT 581
Topics in stochastic simulation including; random number generators; Monte Carlo methods, resampling methods, Markov Chain Monte Carlo, importance sampling and simulation based estimation for stochastic processes. College: School of Engineering Department: Statistics
College: School of Engineering Department: Statistics
Same as STAT 453 with advanced problem sets. Graduate/Undergraduate Equivalency: STAT 453. College: School of Engineering Department: Statistics Pre-requisites: STAT 410 or permission of instructor
This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Graduate/Undergraduate Equivalency: STAT 422. College: School of Engineering Department: Statistics
This course will focus on providing diverse mathematical tools for graduate students from statistical inference and learning; graph theory, signal processing and systems; coding theory and communications, and information theory. We will discuss exact and approximate statistical inference over large number of interacting variables, and develop probabilistics and optimization-based computational methods. We will cover hidden Markov models, belief propagation, Monte Carlo sampling algorithms, and variational Bayesian methods. Cross-list: ELEC 633. College: School of Engineering Department: Statistics
Survey of ideas, methods, and tools for analyzing large data sets; techniques for searching for unexpected relationships in data. Topics from supervised and unsupervised learning include regression, discriminant analysis, kernels, model selection, bootstrapping, trees, MARS, boosting, classification, clustering, neural networks, SVM, association rules, principal curves, multidimensional scaling, and projection pursuit. College: School of Engineering Department: Statistics
This advanced graduate course will address critical evaluation of data through visualisation. The focus is on statistical graphics, graphics that display "statistical" data (observations are in rows and variables in columns), with some forays into the field of information visualisation. Recommended Prerequisite(s): STAT 541. College: School of Engineering Department: Statistics Pre-requisites: STAT 405 AND STAT 410 AND STAT 431
This course centers on applications of statistics in genetic problems, especially as they pertain to genotype-phenotype association. Various data structures will be the centerpiece of the course, including genotype, allele-sharing, and gene-expression. Topics include family and population-based study design, linkage, association, differential gene expression. Genetic analysis software will also be discussed and used. *The course will meet at M D Anderson Cancer Prevention Building, CPB4.3650.* College: School of Engineering Department: Statistics
Stochastic modeling of signaling pathways; models of cancer growth and spread (stochastic and spatial); stem cell differentiation dynamics; viral infection and the immune system, plus background topics (dispersed) such as Markov processes, diffusion equations, branching processes and other. College: School of Engineering Department: Statistics Pre-requisites: STAT 552
We propose to discuss development & application of statistical methods in the analysis of high-throughput bioinformatics data that arise from problems in medical research, in particular cancer research, molecular and structural biology. We present a broad overview of statistical inference problems related to three main high- throughput platforms: microarray gene expression, serial analysis gene expression (SAGE), and mass spectrometry proteomic profiles. Our main focus is on the design, statistical inference and data analysis, from a statistician's perspective, of data sets arising from such high throughput experiments. College: School of Engineering Department: Statistics
This course is an introduction to the statistical and bioinformatic analysis of microarray data. The course covers both Affymetrix oligonucleotide arrays and two- color fluorescence cDNA microarrays. The course introduces students to the full range of processing microarray experiments, from experimental design, through image processing, background correction, normalization, and quality control to the downstream statistical analysis of differential expression. The course includes coverage of the key statistical concept of multiple testing. The covers common methods of pattern identification and pattern recognition in the context of microarrays. It also includes the bioinformatic interpretation of the results through tools to interact with public genome databases. All concepts will be illustrated through hands-on interaction with publicly available microarray data sets. Homework assignments will require some knowledge of R, a statistical programming language. The course will include a brief introduction to R. This class meets in the GSBS library (BSRB 53.8351). College: School of Engineering Department: Statistics
Statistical inference, decision theory, and simulation as applied to assist in making individual clinical decisions, policy recommendations, and as a guide to study design and research; topics include statistical decision theory, decision analysis, decision trees, markvo models and simulation, cost-effectiveness analysis, meta-analysis, and sensitivity analysis. Grading will be based on regularly assigned homework exercises and term projects. College: School of Engineering Department: Statistics Pre-requisites: STAT 422 AND STAT 410 or permission of instructor
Each student enrolled in this course will present scientific articles to the instructor and the rest of the class. These articles will describe statistical methods or novel applications of statistics. Possible topics for these presentations include journal articles or, in the case of more advanced students, current research. The course instructor and other students provide constructive comments regarding the content of the presentation and the presenter's style.
Bayesian Data Analysis
Rosner, Gary. Three semester hours. Fall. Prerequisite: Calculus, linear algebra, prior probability and statistics course (or permission of instructor)
This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data.
Course material will include the basic statistics usually found in introductory courses (t-tests, chi-square, contingency tables) but also will include a balanced emphasis on nonparametric methods, the analysis of variance and covariance through multi-way and hierarchical designs, and regression analysis from simple linear regression analysis through nonlinear methods. The use of personal computers and commercially available programs in the statistical analysis is emphasized in a computer laboratory. Presentation methods, graphics, and statistical word processing are also emphasized. Fulfills GSBS quantitative area breadth requirement.
Shen, Yu. Three semester hours. Spring, odd-numbered years. Prerequisite: Introduction to Biostatistics and Bioinformatics (GS010033) or consent of instructor.
Survival data are commonly encountered in scientific investigations, especially in clinical trials and epidemiologic studies. In this course, we will discuss commonly used statistical methods for the analysis of failure-time data. One of the primary topics is the estimation of survival function based on censored data, which include parametric failure-time models, and nonparametric Kaplan-Meier estimate of the survival distribution. We will also discuss estimation of the cumulative hazard function. Moreover, we will cover the context of hypothesis testing for survival data. These test include the log rank test, generalized log-rank tests, and some non-rank based test statistics. The most applicable to clinical trials and applied work is regression analysis for censored survival data. We will include the Cox proportional hazard model, additive risk model and other alternative modeling techniques. Finally, we will also discuss a number of new theoretical and methodological advances in survival analysis.
Introduction to Biostatistics and Bioinformatics
Yuan, Ying. Three semester hours. Spring annually. Prerequisite: Calculus and linear algebra.
This course is a one-semester overview of statistical concepts most often used in the design and analysis of biomedical studies. It provides an introduction to the analysis of biomedical and epidemiological data. The focus is on non-model-based solutions to one sample and two sample problems. The course also includes an overview of statistical genetics and bioinformatics concepts. Because this course is primarily for statistics majors, the applied methods will be related to theory whenever practical. Students will gain experience in the general approach to data analysis and in the application of appropriate statistical methods. Emphasis will be on the similarity between various forms of analysis and reporting results in terms of measures of effect or association. Emphasis will also be given to identifying statistical assumptions and performing analyses to verify these assumptions. Because effective communication is essential to effective collaboration, students will gain experience in presenting results for statistically naive readers.
Linear Regression and Statistical Computing
Shete, Sanjay. Three semester hours. Fall annually. Prerequisite: introductory statistics, or permission of instructor.
This course will cover basic linear regression analysis. Topics to be covered include simple and multiple regression, diagnostics, influence, and model construction. The emphasis will be on the practical aspects of the construction and validation of linear models. The course will include extensive samples of the use of computer software to perform such analyses. The statistical package R will be used primarily for these examples, although other packages will be illustrated as well. (Students will be permitted to use whatever software they prefer for class assignments.)
This course is intended as an applied introduction to regression analysis. Theoretical results will be developed and presented as necessary, but the emphasis will be on applications.
Mathematical Statistics I
Berry, Donald. Three semester hours. Spring annually. Prerequisite: Advanced undergraduate course in probability and statistics (300 level); probability theory and the central concepts and methods of statistics
A review of probability theory, including generating functions, common families of distributions, multivariate distributions, and hierarchical modeling. Foundations of statistical inference, including sampling distributions, principles of data reduction, maximum likelihood methods, point and interval estimation, hypothesis testing, and decision theory. Applications to advanced statistical problem sets.
Mathematical Probability I
Rosner, Gary. Three semester hours. Fall annually. Prerequisite: Calculus, real analysis, or permission of the instructor
This course is the first of a two-semester sequence covering advanced concepts in mathematical probability. Students learn the measure-theoretic foundations of probability. Topics covered include sigma-fields, probability spaces, random variables, measures, measurable functions, expectation, integration, convergence theorems, product spaces, Fubini's theorem, and convergence concepts.
Introduction to Mathematical Statistics
Cook, John. Three semester hours. Spring. Prerequisite: Introduction to Mathematical Probability (GS010213), or permission of instructor
This course is the second of two courses intended to establish a theoretical foundation for the biostatistics and biomathematics curriculum of GSS. The material introduced in this course is a necessary prerequisite for courses in informatics, survival analysis, and advanced Bayesian inference. The focus will be on integrating both classical and Bayesian methods in a comprehensive but elementary survey. This course will discuss the general approach to statistical inference for data arising from an unknown probability distribution. Students will learn methods for characterizing specific properties of the distributions and use them in making future predictions. The course will discuss statistical inferential methods for data arising from continuous or discrete distributions.
Almeida, Jonas. Three semester hours. Spring, annually. Prerequisite: Consent of instructor.
This course is organized to equip students with both fundamental and practical knowledge of computational tools to develop and/or use web-based bioinformatics tools for data management and analysis. The structure of the coursework starts with introducing the students to scientific computing programming environments and approaches where web-services and semantic web integrative environments can be integrated through the deployment and distribution of computational applications. The classes and teaching style are very much hands-on with evaluation based solely on weekly homework assignments (2/3) and a final presentation (1/3). The course work was designed to start with familiarization of the technological basis for identifying data structures, representing them using interoperable formats, and processing them using standard computational statistics libraries. The students are strongly encouraged to bring and discuss datasets they want to analyze as part of their own research interests.
Statistical Methods in Bioinformatics
Liu, Yin. Three semester hours. Fall, annually. Prerequisite: Introduction to Mathematical Statistics (GS010113) or consent of instructor.
The objective of this course is to introduce students to the concepts and statistical methods for analyzing large-scale biological data generated from emerging genomic and proteomic techniques. The course will focus on the integration of two disciplines - biology and statistics by first describing statistical methods most often used in the field of bioinformatics and then discussing their applications on the computational analysis of gene sequence, expression and biological interactions at a large scale. The statistical methods covered include dynamic programming, maximum likelihood estimation, Bayesian inference, Hidden Markov Models, Markov chain Monte Carlo, classification and clustering methods. The students will master advanced applications of statistical computing in a wide range of biological and biomedical problems, including multiple sequence alignment, biomarker and disease gene identification, inference of protein interaction network, functional modules and signal transduction networks.
Baggerly, Keith. Three semester hours. Fall biannually. Prerequisite: Consent of instructor
This course is an introduction to the statistical and bioinformatic analysis of microarray data. The course covers both Affymetrix oligonucleotide arrays and two-color fluorescence cDNA microarrays. The course introduces students to the full range of processing microarray experiments, from experimental design, through image processing, background correction, normalization, and quality control, to the downstream statistical analysis of differential expression. The course includes coverage of the key statistical concept of multiple testing. The course covers common methods of pattern identification and pattern recognition in the context of microarrays. It also includes the bioinformatic interpretation of the results through tools to interact with public genome databases. All concepts will be illustrated through hands-on interaction with publicly available microarray data sets. Homework assignments will require some knowledge of R, a statistical programming language. The course will include a brief introduction to R. In addition to the biweekly assignments, student performance will be assessed through presentation of a final project.
Mathematical Statistics II
Rosner, Gary. Three semester hours. Fall. Prerequisite: Mathematical Statistics I (GS010083)
This course is a continuation of Mathematical Statistics I. In this semester, the course covers the foundations of statistical inference, including the theory of point and set estimation, hypothesis testing; linear models, asymptotics, decision theory, and Bayesian theory.
Introduction to Mathematical Probability
Cook, John. Three semester hours. Fall annually. Prerequisite: consent of instructor
This course provides an introduction to elementary probability theory. The course discusses the history of probability theory and presents the basic ideas of probability theory in an axiomatic framework. The idea of a probability space is presented, and the idea of a random variable is given. Also included are conditioning and independence, and an introduction to Markov chains. The course also presents the idea of a function of a random variable and how to find its distribution and moments. Finally, the course presents probability distributions (discrete and continuous).
Topics in Clinical Trials
Lee, Jack J. Three semester hours. Spring odd numbered years. Prerequisite: Prior courses in probability and statistics, permission of the instructor
This course deals with fundamental concepts in the design of clinical studies ranging from early dose-finding studies (phase I) to screening studies (phase II) to randomized comparative studies (phase III). The goal is to teach the statistical issues involved in clinical trials, to introduce various clinical trial designs, and to prepare the student to read the clinical trial literature critically. Additionally, the faculty will introduce newer designs for clinical studies that incorporate prior knowledge and/or satisfy optimality considerations. Topics include basic study design options, sample size calculation, randomization, trial conduct, interim monitoring, data analysis, adaptive designs, multiple endpoints, meta-analysis, decision analysis, Bayesian methods, innovative phase I, II trial designs, and writing up the results of a clinical trial for publication.
Course Offerings in Biophysics, Medical Physics, and Nuclear Medicine
Radiation Detection, Instrumentation, and Data Analysis
Howell, Rebecca. Three semester hours. Spring annually. Prerequisite: Introduction to Medical Physics I(GS020093) or equivalent and consent of instructor.
A study of the characteristics and applications of charged particle, photon, and neutron detectors will be covered. Modular analog and digital electronics required for signal processing and data recording will be used. Techniques of data analysis and error propagation of counting statistics will be introduced. The course will include two lectures and one laboratory exercise weekly. The applications of radiation detectors in radiotherapy, health physics, nuclear medicine, and radiobiology will be emphasized.
Course Offerings in Human Genetics
Methods in Genetic Epidemiology Linkage
Amos, Christopher. Three semester hours. Spring annually. Prerequisite: Genetics and Human Disease (GS110013)
This course offers practical experience in the analysis of genetic marker data. The course will cover the basic theory behind linkage analysis and will focus on learning analysis techniques and computer packages.
Fu, Yun-Xin; Xiong, Momiao. Two semester hours. Spring annually. Prerequisite: Genetics, statistics, and consent of instructor.
This course will discuss the principles of population genetics and their applications to human populations as well as statistical methods for analyzing genetic samples of individuals from one or more populations. Topics to be covered include random mating, linkage, inbreeding, natural selection, maintenance of polymorphic and deleterious genes, molecular evolution, quantitative genetics and a modern population genetics approach known as coalescent theory, the cornerstone for analyzing DNA sequence samples from populations. Topics may vary from year to year with the background of the students. Studies at the molecular level will be emphasized.
Data Mining Methodology
Rodin, Andrei. Three semester hours. Summer annually. Prerequisites: Introductory statistics, genetics, basic math and algebra skills.
In this course we will cover application of various novel data mining, machine learning and artificial intelligence methods to the data analysis of large genetic epidemiology datasets. The emphasis will be on the data analysis in wide-scale (genomic, or genome-wide) association studies of complex diseases (such as CVD, or cardiovascular disease), where large numbers of small effects present numerous problems to the traditional statistical methodology. Among other methods, feature construction and feature set reduction, classification, clustering and dependency modeling will be detailed. For comparison purposes, we will also briefly cover (1) applications of the same novel methodology in different but related fields (such as gene expression studies), and (2) more traditional approaches to genetic epidemiology data analysis (such as multiple testing corrections).
Fu, Yun-Xin; Xiong, Momiao. Two semester hours. Fall annually. Prerequisite: Genetics, calculus, statistics, and consent of instructor.
This course is designed as an introduction to statistical genetics/computational biology, and serves as the entry point to several courses in this area. It reviews the key statistical concepts and methods relevant to statistical genetics, discusses various topics that have significant statistical component in genetics, particularly in population and quantitative genetics. Topics include estimation of gene frequencies, segregation analysis, test of genetic linkage, genetics of quantitative characters, inheritance of complex characters, forensic science and paternity testing, phylogeny and data mining.
Introduction to Genomics and Bioinformatics
Barton, Michelle. Three semester hours. Fall annually. Prerequisite: Working knowledge of genetics and molecular biology; access to wireless-capable computer during videocast lectures and labs.
Minimum computer requirements are access during class and for homework to a computer using a Mac, Windows or Linux operating system, which supports Web-based searches, e-mail and Internet access to a Yahoo group site. Computer-assisted quantitative analysis of biological data. A survey of literature and molecular databases. Data retrieval, methodology of sequence alignment and gene identification, phylogenetics, basics of genomic and proteomics. Individual research projects using bioinformatics packages. Lectures and class schedule will follow University of Houston academic calendar (http://www.uh.edu/academics/catalog/general/cal_f07.html). Students must provide their own computers during class meetings and for assignments. Computers must have Windows, Mac or Linux operating systems with capacity to access e-mail and Web browsers.
Genetic Epidemiology of Chronic Disease
Hanis, Craig. Two semester hours. Spring annually. Prerequisite: none
This course will expose students to the evidence and logic involved in inferring the contribution of genetic mechanisms to those diseases of public health importance. Emphasis will be on developing a framework for assessing the impact of genes on common disease, but will not include detailed methodological developments or statistical techniques. The format will be a weekly 2 hour session in which a single disease will be examined. In this way students will be exposed to a broad spectrum of diseases and see the similarities of the problems inherent to each and also the uniqueness of each.
Genetic Epidemiology: Association Studies
Mitchell, Laura. Two semester hours. Spring annually. Prerequisite: none
This introductory level course in genetic epidemiology focuses on the design of studies to identify disease-gene associations. The lectures concentrate on the two most common study designs for genetic association studies: case-control studies and case-parent trios, and addresses disease-gene associations, gene-environment interactions and maternal genetic effects. Students will learn about study design and data analysis through class lectures, independent readings, completion of problem sets and class discussions. Relevant software will be discussed and applied.
Course Offerings in Pharmacology and Toxicology
Cavasotto, Claudio. Three semester hours. Spring annually. Prerequisite: Consent of instructor
The aim of this 3-hours credit course is to introduce the foundations of biomolecular modeling and chemoinformatics, their use in the context of drug discovery, and present the most used computational techniques. Topics are covered in a hierarchical way, from basic concepts to application in drug discovery, combining lectures, hands-on sessions and review of key scientific papers. The student will learn about computer representation of molecular systems, concepts of drug discovery and medicinal chemistry, protein modeling, ligand-based and receptor-based drug discovery, post-screening and bio-evaluation, lead optimization, chemical databases, quantitative structure-activity and structure-property relationships, and molecular dynamics. Students will enhance their research by incorporating concepts of computer representation and modeling of biomolecular systems, thus being better prepared to interact with their computational/modeling colleagues and collaborators.
This course introduces molecular biologists to software tools on the World Wide Web for search, retrieval and analysis of amino acid and nucleotide sequences. The software includes BLAST, FASTA and Clustal_W. The course also covers secondary and tertiary structure prediction of proteins, homology or comparative modeling and methods for the analysis and validation of structures. 3-D modeling based on experimental & theoretical constraints will be explained with special reference to software including MASIA, EXDIS, DIAMOD & FANTOM. Two sessions will be given per week. The first session will describe the scientific concepts behind the tools and the computational procedures. The second session will be an “I day” where students will learn to use these tools through hands on sessions on modern graphic workstations. The goals are to provide a critical appreciation of bioinformatics tools and enable the student to apply them to his/her research. This is a 10 week course. Instructors: W. Braun Term offered: II Year offered: Annually Hours per week: Lecture 2
Lecturers will select seminal recent papers on principles and novel techniques used in the interpretation of DNA micro arrays, protein arrays and data mining of structural and functional databases. Each student is requested to read all papers, and present one paper with additional background information in a 45-minute lecture. The faculty will provide additional advice on the context of this paper in the literature, might complement the student presentation with comments from his expertise on particular techniques, and will stimulate the discussion on the content of paper. Prerequisite: Consent of instructor or BBSC core Instructor: Braun Term offered: I Year offered: Annually Hours per week: 2, 10 Week Course
This seven-week course is a required core course in the Basic Biomedical Science Curriculum (BBSC) and is a brief introduction to statistical thinking. Specific topics include over-view of basic summaries, probability and distributions, inference, experimental design and linear models. Grading will be based on the performance on homework, a take- home mid-term examination and an in-class examination. Prerequisites: BBSC 6401, BBSC 6402, BBSC 6403 or consent of instructor Term offered: III Year offered: Annually Hours per week: Lecture 3 Instructor: Spratt