Biomedical Informatics Courses
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A survey of biomedical informatics theories and methods employed in the design, implementation and management of information systems supporting basic science, clinical and translational research, clinical care, and public health. Recommended coursework in computer science, statistics, anatomy, physiology, and medical terminology.
Although it is not required, a background or experience with computer science, statistics, anatomy, physiology, and/or medical terminology is strongly recommended.
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Introduces students to basic topics of bioinformatics including sequence analyses, proteomics, microarrays, regulatory networks, sequence and protein databases. Recommended background in molecular biology and computer science.
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The course will introduce trainees to the basic theories and methods employed during the design, implementation, and management of systems used to collect, exchange, store, query, and analyze large-scale, heterogeneous biomedical data sets. Examples of information systems to be discussed include clinical trials management systems, tissue repository management systems, collaborative “team-science” tools/platforms, and integrative data discovery and analysis tools/platforms.
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This four-week course educates trainees in practical biomedical informatics, study design, statistical analysis, and computational techniques related to biomedical research. The course will provide applied primers covering foundational biomedical informatics and quantitative science methods employed in the design, conduct, and analysis of basic science, clinical, and translational research programs. This survey course is intended to enable individuals to critically select such methods and evaluate their results as part of both the design of new projects as well as the review of results available in the public domain (e.g., literature, public data sets, etc.).
- Core concepts to be reviewed during this course include:
- Basic computational skills (R programming)
- Data integration (data transformation / merging / manipulation, metadata integration)
Basic probability (conditional probability, Bayes theorem, probability distributions, sampling distributions)
- Study design principles (population and sample selection, study design principles)
- Exploratory analysis of data (graphical displays of data, data summarization)
- Statistical analysis of data (estimation, confidence intervals, hypothesis testing, regression, two-group tests, analysis of variance (ANOVA), survival analysis)
- Power and sample size calculations
- In silico hypothesis generation (data mining, text mining, and visualization)
- Introduction to data and methods in bioinformatics (clustering, classification, RNA-seq data analysis)
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Introduction to the emerging and critical field of public health informatics. This course will highlight the history, current and future use of informatics in public health settings, and give students an understanding of the role and broad application of informatics in promoting health and preventing disease.
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This online course is a hands-on exploration that walks students through theory and practice of exploring how data can be leveraged to facilitate discovery. The course offers an introduction to the emerging field of health analytics—the use of health-related data to improve the lives of people, processes and organizations. In this course, we will explore the data ecosystem, wrestle with the challenge of finding questions in that data, explore how we frame answers to questions using available tools and then practice presenting that data in meaningful ways.
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This course will offer the foundational and working knowledge, skills and tools for programming for Biomedical informatics. This course will focus on python, High-Performance Computing, and github, as these are commonly used in Biomedical Informatics. This course will cover the elementary Python programming (e.g., python syntax, environment, libraries) with which students should be able to write and run basic python programs. This course will also cover foundational data structures that are useful in Biomedical Informatics research (e.g., graphs, trees, matrices) and corresponding concepts and algorithms over the data structures (e.g., graph flow, tree traversal, matrix operations). With these data structures and algorithms, students should be able to implement many popular methods from scratch. In addition to data structures and algorithms, the course will discuss basic data analytics methods (e.g., classification, regression) and their implementations in popular python libraries for Biomedical Informatics applications. This course will also cover other useful content including basic commands and operations in Linux system, the access and use of High-Performance Computing (HPC) systems, use of github for open-sourced code sharing and version control, formal documentation, etc. This course will offer working knowledge and practice opportunities of popular Python tools, packages and systems such as scikit-learn and pytorch, and programming skills using pytorch. Out of this course, the students should acquire necessary programming knowledge and skills, and be ready to program in Python and implement popular data analysis methods that can be used in Biomedical informatics research.
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This course aims to introduce students to the principles and practice of metabolomics. Metabolomics is the study of the totality of small molecules existing within a system. We will focus here on the application of metabolomics to plant, food, nutrition and health-related research, although concepts are applicable to other disciplines. Each part of the metabolomics workflow will be covered, with hands-on experience in sample preparation, data collection, data processing and analysis, modeling, contextualization and validation. The course will also contain a journal-club component where students choose work from the primary literature and briefly explain it to the class during week 2 and then present a deeper, critical review at week 15, incorporating what they’ve learned throughout the course.
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The course is an introduction to research design and methods in biomedical informatics. It is organized around elements of proposal writing, grant writing, and 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 culminating project will incorporate writing elements of an NIH fellowship grant proposal.
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The goal of this course is to let students get familiar with the commonly used bioinformatics data analysis tools via hands-on training and discussion on both classical and state-of the-art literature. The topics include integrative analysis and visualization of microarray and NGS data for genotyping, genomics, proteogenomics, and from WSI studies towards human diseases as well as advanced methods based on gene network inference and analysis.
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This course provides training in the theories, methods and application of clinical informatics—the field concerned with the use of data and information technology applied to the delivery of healthcare services. Clinical informatics has a wide array of healthcare delivery application areas in the clinical domain including pharmacy, nursing and patient care operational areas.
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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, 11:00 AM - 12:00 PM) or from the seminar series, which brings in external speakers to present on their research (schedule is listed on departmental calendar).
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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). The 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 the use of R packages that can be applied to these kinds of problems.
It is expected that students have basic knowledge of the following areas:
- Computer science principles (logic, procedural and/or object oriented programming, data structures and algorithms).
- Statistical methods.
- Biomedical terminology.
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This course will cover theory and concepts of empirical tools to describe disease burden, health state utilities, patient-reported outcomes, patient experience, and patient preferences. The course will also cover important policy contexts that govern the study of patient preferences (e.g. regulatory, pricing/reimbursement) and good research practice documents that guide empirical studies of the patient experience.
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This course has the two goals of teaching students in all aspects of life sciences how to computationally analyze datasets and to instill 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 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.
Independent Studies and Research Credits
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Enables upper-level undergraduate and graduate students to do research projects with faculty other than their adviser. Students should get in touch with faculty whose research aligns with their academic interests to enroll in private studies.
Prereq: Permission of instructor. Repeatable to a maximum of 60 credit hours or 4 completions. This course is graded S/U. -
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.
Pre-req: Permission of instructor. Repeatable to a maximum of 99 credit hours or 20 completions. This course is graded S/U.
Special Topics
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Course Director: Lang Li, PhD and Xia Ning, PhD
Class Time and Location: Tuesdays and Thursdays, 3:55 – 5:15 PM; Virtual classes
The goal of this course is to introduce trainees to the natural language processing and text mining on biomedical data, including clinical notes from electronic medical records and biomedical literature. Examples of major topics to be discussed in this class include: 1) gold standard annotations on the text, i.e. corpus construction; 2) NLP tasks, including text classification, part of speech tagging, chunking, syntactic annotation, semantic annotation, named entity recognition, and relationship extraction; 3) text data processing, feature generations and representation learning; 4) machine learning and deep learning methods, 5) the applications of machine learning and deep learning methods in NLP; and 6) software.
While there are no strict requirements, a successful student should have basic knowledge of the following areas: 1. Programming skills in R. 2. Some knowledge in machine learning.
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Class time and location: Mondays, 2:15-5:00 pm; Virtual classes
Course description: Artificial Intelligence (AI) and Machine Learning (ML) provides an unprecedented opportunity to accelerate and revolutionize human health and the pace of clinical and translational science. The purpose of this course is to train the next generation of the translational medical workforce by teaching them the primary ML and AI algorithms used in bioinformatics and computational biology. We will cover the theoretical underpinnings of the methodology along with an explanation of how to use practical implementations (in R or python) of how to apply the methods to real bioinformatics data sets. An important goal of the course is to introduce students to more advanced algorithms that are not covered in other classes in BMI. Examples include modern regression techniques (including ridge regression, lasso, and elastic nets), deep learning (CNN, RNN, GNN using TensorFlow), non-linear dimension reduction (including t-SNE and ISOMAP), directed and undirected graphical models, and association rules. It is noteworthy that the class will have a special emphasis on the fundamentals and applications of deep learning and provide a conceptual understanding of deep learning with a holistic view and latest developments in the field. By the end of the course, students will have had practice applying all of these methods to actual data sets.