School of Medicine

  BIOINF 2011, Introduction to Biomedical Informatics

A survey of fundamental concepts and activities in medial information processing.  Topics include information systems in the areas of medical records, nursing, radiology, patient monitoring, pharmacy services, clinical laboratory services, bibliographic and full-text storage and retrieval, clinical decision support, clinical research and medical education. Issues of information processing in health care financing, legal, ethical and philosophical issues in medical informatics.

  BIOINF 2051, Bioinformatics I: Introduction to Bioinformatics

This course is designed to provide an understanding of some important topics in bioinformatics and computational biology. Students will be able to learn about problems involved in the generation and analysis of biological data such as DNA/protein sequences and protein structures. The course is intended to provide a fairly good understanding of the commonly used algorithms in the analysis of genomic data, and hands-on experiences with accessing and using relevant databases. Emphasis is on genomic aspects of computational biology with some overview of proteomics and structural aspects.

  BIOINF 2052, Bioinformatics II: Introduction to Computational Structural Biology

This course is a general introduction to current theories and methods used in computational structural biology. Fundamental concepts of probability, statistics, statistical thermodynamics and polymer physics will be considered as well as a general description of our current knowledge of biomolecular structure and dynamics for modeling and simulations of biological interactions and function. The Protein Data Bank and software commonly used in computational structural biology will be used for modeling and simulations of structure and dynamics.

  BIOINF 2053, Bioinformatics III: Computational Biology Laboratory

This course will comprise summer workshops offered through the Pittsburgh Supercomputing Center, and will provide hands-on experience within sub-areas of computational biology.

  BIOINF 2054, Statistical Foundations for Bioinformatics Data Mining

This course introduces data analysis methods which are widely used or rapidly gaining use in bioinformatics. Methods deal with prediction, classification, optimization, and clustering. Methods covered include classification trees, flexible varieties of discriminant analysis including support vector machines, EM algorithm and Monte Carlo Markov chain, the bootstrap and bagging, boosting, and self organizing maps. The methods are placed into the context of principles and models of statistical science, with emphasis on Bayesian methods. Examples are drawn from microarrays, analysis of genetic networks, proteomics, computational pharmacology, and research text mining.

  BIOINF 2055, Practical Analysis of High-Throughput Genomic and Proteomic Data Sources

This course provides an in-depth, comparative study of methods for the analysis and interpretation of high-throughput genomic and proteomic data sources. Using a broad survey of the literature, the student will become familiar with approaches to normalization/transformation, finding predictive biomarkers, methods for classification, cross-validation, functional interpretation. Ways to integrate diverse data sources, including clinical outcomes, will be explored. Classroom activities will include lectures exercises in the use of publically-available software, and intensive experience in the analysis and interpretation of published data sets. By the end of the semester, students will be able to think critically about the diverse strategies for analyzing high-throughput genomic and proteomic data sources.

  BIOINF 2081, Distinguished Lecture Series in Bioinformatics

This course invites researchers in computational biology and bioinformatics from all institutions in the Pittsburgh region to present their research, with emphasis on the fundamentals of the primary methodologies used. Past topics have included protein folding, quantitative structure activity relationship (QSAR) methods in drug discovery, spatial modeling of cellular microphysiology.

  BIOINF 2100, Evaluation Methods for Medical Informatics

This course is designed to expose students to the wide range of empirical evaluation and research methods used in medical informatics, to immerse students in the empirical literature of the field, and to prepare students to design and conduct studies appropriate to problems in the field.

  MSBMG 2510, Biochemistry of Macromolecules

Distinguished Lecture Series in Bioinformatics - This site shows the schedule for the Distinguished Lecture Series in Bioinformatics at the University of Pittsburgh.