Uniformed Services University of the Health Sciences
4301 Jones Bridge Road
Bethesda, Maryland 20814-4799
Phone: (301) 295-2163
FAX: (301) 295-3585
Pamela Cross, Secretary
The Biomedical Informatics Department is located on the ground floor of the main campus, around the corner from the Security Office that's accessible from the USU parking garage tunnel entrance.
Our department is working toward expanding its educational offering each year. We currently offer courses that are open to USU matriculated students as well as others (on a not-for-credit basis).
Bioinformatics (BID 500): The goal of this course is to provide students with an overview of the databases, information systems and web-based technologies presently available for biological information and to survey tools available to process the information. Some emphasis will be placed on using web-based and stand-alone tools for both DNA and protein sequencing analysis, providing students with the knowledge and skills to immediately use the tools and/or move on to higher level courses. (Dr. Williams-spring term).
Public Health Informatics (BID 501): The course provides students with a conceptual framework for understanding the emerging field of public health informatics. The course will include in-depth discussion of technology in the practice of public health with emphasis on preparing for and responding to manmade and natural disasters and emerging infectious diseases outbreaks. The course will also highlight successes and failures in implementing public health informatics projects and the critical role that leaders play in each step of the developmental process from idea inception through systematic implementation. This course is a product of an emerging collaboration between the Uniformed Services University and the National Library of Medicine. (Drs. Hakkinen & Johnson-spring term).
Patient Safety & Quality in an IT-driven World (BID 502): This course integrates theory and conceptual frameworks at use in patient safety, clinical quality, and in implementation science. The course is designed for students (both clinically-oriented and non-clinical) with patient safety & quality exposures but minimal understanding of health informatics and for those with health informatics exposure but minimal introduction to patient safety & quality. The curriculum includes three basic building blocks: 1) basic theories, frameworks, and concepts in patient safety & quality, 2) health information systems and technologies, and 3) themed challenges in patient safety & quality being addressed with technology solutions (either exclusively or as a component). The course will emphasize advancing the science of patient safety & quality. The course is a part of an emerging partnership between the Agency for Healthcare Research & Quality and the Uniformed Services University focused on the delivery of education in patient safety & quality. (Drs. Gimbel, Brady & Battles - spring term).
Introduction to Health Informatics (BID 503): The course provides future health care leaders a conceptual framework for understanding medical informatics and information technology as applied in the healthcare environment. The course will include in-depth discussion of technology in health care systems with emphasis on leveraging technology to improve quality and efficiency in care delivery. The course will also highlight successes and failures in implementing health information technology and the critical role that leaders play in each step of the developmental process from idea inception through systematic implementation. This course is a product of an emerging collaboration between the Uniformed Services University and the National Library of Medicine. (Dr. Gimbel - winter term).
Perl Programming for Biologists (BID 504): The purpose of this course is to give students a practical working knowledge of the Perl programming language for applications in biology. Programming skills are becoming increasingly important in biology. From the text: "Advances in high-throughput biology have transformed modern biology into an incredibly data-rich science. Biologists who never thought they needed computer programming skills are now finding that using an Excel spreadsheet is simply not enough." Perl is the most widely used language used for biological problems, there is a very large number of existing Perl programs that can readily be adapted for repeated tasks typically performed by biologists, so it is seldom necessary for biologists to write perl programs from scratch, and "regular expressions" in Perl are applicable in a wide variety of other scripting languages, including Python. (Dr. Williams-fall term).
Applied Statistics for Life Sciences using R (BID 505): The purpose of this course is to give students in the life sciences, epidemiology, biology, and psychology, a practical working knowledge of statistical programming using R. R is a tool designed for statistical analysis and extended to include many of the other tasks necessary for the analysis of statistical data, including data mining, high throughput analysis, neural networks, and other algorithms for pattern analysis.
Students are expected to have an introductory understanding of statistics. Emphasis will be placed on a practical application of R to data taken from the literature and made available in the R packages for the disciplines represented by students in the class. The first part of this course will cover the use of R in statistical analysis in general. Students will subsequently be tracked into specialized topics in their own fields of study.
The complexity of data analysis needed for Bioinformatics and Epidemiology research requires a sophisticated computer data analysis system. It is not true, as often misperceived by investigators, that computer programming languages (such as Java or Perl) or office applications (such as spreadsheets or database applications) can replace a statistical applications package. The majority of functionality needed to perform sophisticated data analysis is found only in statistical software.
The software chosen for this course, the S language implemented in R, has been in active professional development since 1976, and version 4 of S was released in 1998. The development of R began in 1991. It is one of the best statistics data analysis and programming systems available. R is free, making it accessible to students and investigators without imposing a recurring cost and a dependence on commercial software. The architecture of R provides a framework for the addition of smaller specialized program modules called packages. A large number of packages for scientific and public health research are available for R, making it so flexible and extensible that it can unify most (if not all) data analysis tasks in one program with add-on packages. Rather than learn multiple tools, students and investigators can use one consistent environment for many tasks. R is offered in many, if not most biomedical informatics and epidemiology programs and has become the statistics package of choice for bioinformaticians. (Dr. Williams - full term)
Unix and Databases for Biologists (BID 506): The purpose of this course is to give students a practical working knowledge of Unix (Linux and MacOS command line) and SQL databases. From Dudley JT, Butte AJ, 2009 A Quick Guide for Developing Effective Bioinformatics Programming Skills. PLoS Comput Biol 5(12): e1000589. doi:10.1371/journal.pcbi.1000589 -----:
Bioinformatics programming skills are becoming a necessity across many facets of biology and medicine, owed in part to the continuing explosion of biological data aggregation and the complexity and scale of questions now being addressed through modern bioinformatics. Although many are now receiving formal training in bioinformatics through various university degree and certificate programs, this training is often focused strongly on bioinformatics methodology, leaving many important and practical aspects of bioinformatics to self-education and experience.
Given the diversity and complex nature of problems in biology, medicine, and bioinformatics, it is imperative to be able to approach each problem with a comprehensive knowledge of available computational tools – so that the best tools can be selected for the problem at hand. The most fundamental and versatile tools in your technology toolbox are programming languages. While most modern programming languages are capable of any number of computational feats, some are more apt for particular tasks than others. For example, the R language is almost unparalleled in its statistical computing capabilities... While there are many languages that would be appropriate and effective in which to seek mastery for bioinformatics, modern interpreted scripting languages, such as Perl, Python, and Ruby, are among the most preferred and prudent choices.
Even if you don't choose to run a UNIX-based Operating System (OS) on your personal workstation, knowledge of UNIX is tremendously useful in bioinformatics. Although the Windows platform is perfectly adequate for bioinformatics, the simple truth is that the majority of bioinformatics computation happens on UNIX-based computer systems. A portion of this circumstance may be attributable to a tradition of scientific computing on UNIX and the availability of many free, open source UNIX-based OS, such as Linux. Even so, it can be argued that a UNIX-based OS offers several advantages when it comes to facilitating bioinformatics.
The tradition of using flat files in bioinformatics (i.e., storing data records in large text files) is out of step with current needs. In the modern era of integrative biology and medicine, we are often faced with the task of integrating data from multiple sources in complex ways (e.g., relating SNPs, gene expression, and proteomics data to build models of gene regulation). The use of flat files often requires the programmer to load huge numbers of data records into system memory, and then index and join these data using custom program logic. Relational Database Management Systems (RDBMS), such as MySQL, are well suited for such tasks, yet they remain underutilized by many in bioinformatics. The utilization of RDBMS can be intimidating to those without formal database training, as they often require the set-up and management of database server systems, and their contents must be defined and queried using the somewhat peculiar Structured Query Language (SQL). (Dr. Williams)
Python Programming for Biologists (BID 507): The purpose of this course is to give students a practical working knowledge of the Python 2.7 programming language for applications in biology. Programming skills are becoming increasingly important in biology and many biologists are finding that web based applications are not adequate for processing high throughput data or for tasks that require repeated operations. Python is now one of the scripting languages of choice for bioinformaticists. It is easy to learn and used, and can interface with Java, R, and other languages easily. While Perl may be a better choice for writing small programs that get work done, and for re-using code written by others, Python is preferred by many bioinformatics programmers who need to write larger programs. (Dr. Williams-spring term).
Clinical Informatics (BID 4001): This 4 week clinical informatics elective is offered to provide medical students with some education and exposure to clinical informatics. The student(s) will be mentored by the top clinical informaticians currently serving in the Military Health System. The student(s) will receive training on information systems, work alongside the clinical informatics staff developing tools/templates, participate in meetings with clinic personnel about their informatics needs, shadow their preceptor in command meetings with policy implications, and like activities (Dr. Gimbel). [4th year medical student elective]
Introduction to Bioinformatics (MCB 904): The principle objective of bioinformatics is to find the relationships between DNA and protein sequence on the one hand, and biological function and disease on the other. Bioinformatics is the application of computer science and information technology to the field of biology and medicine. The goal of this course will be to give students overviews and practical application skills in the following topics: databases, sequence alignment, gene and regulatory sequence recognition, Perl, Bioperl, R, and Bioconductor programming, and Linux for Biologists. Students will be able to immediately start using these tools, or to move on to courses or tutorials at higher levels.