Biomedical Informatics Training
Basic skills in Bioinformatics are quickly becoming a requirement for successful biomedical research in many disciplines. The Bioinformatics Track will offer participating PhD programs the option of following a modified curriculum that teaches the fundamentals of their chosen PhD program, but offers expanded training in bioinformatics, including commonly used programming languages and computational techniques to facilitate use and interpretation of -omics (genomics, transcriptomics, proteomics, lipidomics, metabolomics, and microbiome) and other data intensive methods. The program also offers students with research projects relying on computational and quantitative methods to come together and discuss the literature, as well as new and state-of-art techniques.
- To provide training in modern computational methods for data intensive scientific projects
- Provide expertise to work with large scale data types in biomedical sciences (including, but not limited to -omics data)
- Learn basics of data visualization and statistical analysis of high throughput data
- Learn to use resources available in the public domain
The students in this track will take three courses during their PhD training. 1) Current Topics in Bioinformatics Research for two semesters or more, 2) one of three offered programming courses (IND 419, BCH 521 or BIO457) and 3) one of the four statistics courses (BST 467, DSC 462, BST 432, or BST 434). We recommend discussing the course selection and timing (semester of your course work) with the thesis advisor and program director. Furthermore, Professor Juilee Thakar, the Director of the Bioinformatics Track will be available to provide advice on a selection of courses that is suitable for individual students.
Current topics in Bioinformatic Research (1 credit): This is a literature-based course that takes place in Spring and Fall semesters. Participants in this course will discuss publications that use large scale data sets and heavily rely on computational approaches. The goal is to learn how current methods can be applied to answer relevant biological and biomedical questions.
Choose one from (IND 419, BCH 521, and BIO 457)
Introduction to Quantitative Biology (IND 419, 3 credits): This course takes place in Spring and introduces frequently used pipelines and analysis techniques using the R programming language. Prerequisites: none
Bioinformatics for Life Scientists (BCH 521, 4 credit): This course takes place in Fall. This course teaches scripting in Python and also algorithm design for bioinformatics. It expects no prior knowledge in programming. The class meets twice a week – once for a traditional lecture and once for a laboratory session. Prerequisites: none
Applied genomics (BIO 457, 4 credit): This course teaches how tools in the fast-moving field of genomics are applied to address important biological problems. Students get hands-on training in genome analysis techniques and functional genomics. Some programming background will help with this course. Prerequisites: BIO 190 or BIO 198 or equivalent
Choose one from (BST 467, DSC 462, BST 432, and BST 434):
Computational Introduction to Statistics (DSC 462, 4 credits): This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment. Prerequisites: MATH 161/ Calculus I
Applied Statistics in the Biomedical Sciences (BST 467, 3 credits): Introduction to statistical techniques with emphasis on applications in the biomedical sciences. Introduction to probability and probability distributions; sampling distributions; estimation, confidence intervals, and hypothesis testing in small and large samples; analysis of categorical data; analysis of variance; correlation and linear and nonlinear regression analysis; use of statistical software; illustrations using published articles in the biomedical sciences. Prerequisites: One semester of undergraduate statistics is required. Prerequisites: one semester of undergraduate statistics.
High Dimensional Data Analysis (BST 432, 4 credits): We will review statistical theory underpinning high-dimensional data analysis as well as develop understanding of machine and statistical learning techniques for prediction (of an unknown label, or a new data point) and inference (of the parametric state of a system). Computational algorithms are important to accomplish these goals, and will be emphasized. We cover both classical and modern techniques, as well as methods to derive unbiased estimates of predictive performance including cross-validation. Prerequisites: BST 401 and BST 411 or DSC 462 or equivalent
Genomic Data Analysis (BST 434, 4 credits): Introduction to techniques used in modern genomic experimentation and the corresponding statistical methods and software available to visualize, analyze, and interpret these data. Specific topics include mRNA/microRNA expression, protein abundance, protein-DNA binding, copy number variants, single nucleotide variants, DNA methylation, and microbial abundance. Prerequisites: BST 401 and BST 411 or DSC 462 or equivalent