BMI 5770 - Health Analytics: Data Discovery to Dissemination
Director: Bobbie Kite, PhD, MHS
Synopsis: Health Analytics is the science of analyzing health data for knowledge discovery and decision making. The sheer diversity of data types in health care settings results in what scholars call a DRIP environment: Data Rich-Information Poor. Data has become ubiquitous in healthcare settings from clinical decision making to operational/business planning; health decisions are now being made similarly.
No programming experience is required, but students are expected to vigorously engage in their own learning and parts of courses will require that students use and adapt to tools demonstrated during class.
Director: Po-Yin Yen, RN, PhD
Schedule: Monday/Wednesday: 9:35 - 10:55 AM
Location: 245 Lincoln Tower
Synopsis: This course is an introduction to research design and methods in Biomedical Informatics. It is organized around elements of qualitative and quantitative study design. We will be surveying aspects of research, including the formulation of research questions, testable hypotheses, the selection of appropriate research designs and methods, data collection and analysis. The course objectives are centered around the development of a full-scale research project aligned to the National Institute of Health (NIH)'s guidelines for review of grant submissions: Significance, Investigators, Innovation, Approach, and Environment.
Director: Ewy Mathé, PhD
Schedule: Seminars are Fridays, 1:00 - 2:00 PM
Synopsis: The purpose of this course is for faculty and external guest speakers to give presentations on current BMI research and theories critical to the advancement and awareness of biomedical informatics within the healthcare and research communities. Alternate classes will consist of journal-club style discussions moderated by faculty, in which trainees will present on their current research projects.
Trainees will choose a select number of seminars to attend as determined by the course director. Seminars can be chosen from either the departmental CALIBRE seminar series (Fridays, 1:00 - 2:00 PM) or from the seminar series, which brings in external speakers to present on their research (schedule is listed on departmental calendar).
This course is geared specifically towards students and trainees working or taking coursework within the BMI department.
Directors: Ewy Mathé, PhD and Kevin Coombes, PhD
Schedule: Tuesday/Thursday, 9:35 - 10:55 AM
Location: 245 Lincoln Tower
Synopsis: The goal of this course is to introduce trainees to the fundamental algorithms needed to understand and analyze genome-scale expression data sets. The course will cover three major kinds of applications. (1) Class Comparison seeks to describe which features differ between two or more known classes of patient samples (such as normal vs. tumor). Methodology includes (generalized) linear models with careful attention to the issue of multiple comparisons. (2) Class Discovery seeks to discuss the inherent structure present in a data set. The methodology includes a wide variety of techniques for clustering samples (including K-means as well as various forms of hierarchical clustering) and assessing the number of clusters and the robustness of cluster assignments. We also cover methods such as principal components analysis that help visualize the data. (3) Class Prediction seeks to discover and validate models that can accurately predict the class or the outcomes of new samples. Methods include a wide variety of machine learning and statistical methods for feature selection and model construction. We will also discuss methods for cross-validation and independent validation of predictive models. The course will include an introduction to, and hands-on experience with, the R statistical software environment and to the use of R packages that can be applied to these kinds of problems.
Course objectives: Upon completion of this course, students should have: 1) A familiarity with the fundamental algorithms used in bioinformatics; 2) An understanding of the theoretical frameworks justifying those algorithms; 3) The ability to apply those algorithms to real-world problems using R; and, 4) Critical evaluation skills that allow for the analysis and design of bioinformatics approaches to real problems in biology and medicine.
It is expected that students have basic knowledge of the following areas: 1. Computer science principles (logic, procedural and/or object oriented programming, data structures and algorithms). 2. Statistical methods. 3. Biomedical terminology.
Directors: Guy Brock, PhD, Kevin Coombes, PhD, Jeff Parvin, MD, PhD
Schedule: Friday, 2:00 - 5:00 PM
Location: 245 Lincoln Tower
Synopsis: This course has the two goals of teaching students in all aspects of life sciences how to computationally analyze datasets and to inculcate best lab practices in experimental design and analysis. Students will learn the computer language R, and use R to analyze datasets from transcriptome, genome, and clinical studies. Students will develop an understanding of sources of bias and the impact of these biases on results and potential conclusions. Examples will be taken from the literature of experimental designs that were rigorous and that had built-in flaws. At the completion of the course, students will have an intermediate level of competency in R and knowledge of how to manage and analyze large datasets.
Course objectives: Upon completion of this course, trainees with have 1) learned the best practices in the design of experiments and selection of appropriate controls for high rigor and reproducibility; 2) develop realistic and refutable hypotheses; 3) intermediate level competency in the computer language R; 4) practical knowledge of analyzing and managing large datasets, including transcriptomic, genomic, and clinical data; 5) understanding the sources of bias; and, 6) principles of laboratory best practices.
Director: Each research advisor will have an open 7999 and 8999 course each semester for their students to enroll in. Research for thesis or dissertation purposes only.
Prereq: Permission of instructor. Repeatable to a maximum of 99 credit hours or 20 completions. This course is graded S/U.