Spring 2019 Courses


Foundations of Translational Bioinformatics (3 credits)

The course goals are to gain familiarity of data produced with current biotechnologies, such as DNA arrays (e.g. SNP data), microarrays (transcriptional profiles), proteomics (mass spectrometry data), epigenomics (methylation profiles). Understand what can be done with such data to infer relations between genome, epigenome, and phenome, in order to discover molecular mechanisms of diseases, or identify biomarkers, or discover novel therapies for diseases.

Instructor: Xinghua Lu, M.D., Ph.D. and Madhavi Ganapathiraju, Ph.D.

Days/Times: Mondays and Wednesdays, 3:00 p.m. to 4:25 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: None

Recitations: None



Biomedical Informatics Journal Club (ISSP 2083) (1 credit)

Biomedical informatics is a broad field encompassing many different research domains. What all of the domains have in common is the need to review and publish scientific papers and to give talks that present research to different audiences. The aim of this journal club is to expose students to recent research in various topics of biomedical informatics and to teach students how to critique a research article, present research from a research study; and critique a verbal presentation of research.

Instructor:  Xinghua Lu, M.D., Ph.D. and Ervin Sejdic, Ph.D.

Term:  Spring

Days/Times:  Fridays, 10:00 a.m. to 11:00 a.m.

Location:  536B BAUM, 5607 Baum Blvd.



Statistical Foundations of Biomedical Informatics (3 credits)

This is an introductory probability and statistics course intended primarily for biomedical informatics students. The first part of the course covers probability, including basic probability, random variables, univariate and multivariate distributions, transformations, expectation, numerical integration, and approximations. The second part of the course covers statistics, including study design, classical parametric inference, hypothesis testing, Bayesian inference, non-parametric methods, classification, ANOVA, and regression. We will use R for statistical computing and applications. Examples and applications will focus on biomedical informatics and related discipline.

Instructor:  Doug Landsittel, Ph.D.

Term:  Spring

Days/Times:  Tuesdays and Thursdays, 12:00 p.m. to 1:25 p.m.

Location407A BAUM, 5607 Baum Blvd.

Expected class size:  10-15



Probabilistic Methods in Artificial Intelligence (3 credits)

This course is designed for students who do not necessarily have a background in computer science and want to learn and apply methods in artificial intelligence to problems in biomedicine. The course will introduce and provide the foundations artificial intelligence methods in search, probabilistic knowledge representation and reasoning, and machine learning with applications to biomedical informatics. Prerequisites for this course include introductory mathematics and programming.

Instructor:  Shyam Visweswaran, MD, PhD, Xia Jiang, Ph.D. and Madhavi Ganapathiraju, Ph.D.

Term:  Spring

Days/Times:  Monday/Wednesday from 12:30-2:00 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  Introductory Mathematics and Programming

Expected class size:  10-15