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Biomedical Informatics Courses


Course Number Course Name Course Director Days Times

Summer 2019
5750-10 ​Methods in Biomedical Informatics and Data Science ​Brock, Guy ​TR ​9:35-10:55 am ​summer session
5750-20 Methods in Biomedical Informatics and Data Science Zhang, Yan online online ​4-week session 1

Autumn 2019
5710-10 Introduction to Biomedical Informatics Hebert, Courtney/Mathe, Ewy TR 3:55-5:15 pm
5710-20 Introduction to Biomedical Informatics Gascon, Gregg online
​5750 ​Methods in Biomedical Informatics and Data Science ​Zhang, Pengyue ​MW ​​12:45-2:05 pm
5760 Public Health Informatics Motiwala, Tasneem online
7891 Seminars in Biomedical Informatics Au, Kin Fai/Zhang, Ping F 11:00-12:00 pm
8140 Measuring Patient Experience and Preferences Bridges, John F.P. TR 5:30-6:50 pm
8130 Analysis and Applications of Genome-Scale Data Au, Kin Fai TR 9:35-10:55 am

Spring 2020
5730 Introduction to Bioinformatics Cheng, Lijun online
5740 Introduction to Research Informatics Gascon, Gregg online
5770 Health Analytics: Data Discovery to Dissemination Fareed, Naleef online
7040 Clinical Informatics Gregory, Megan/Hebert, Courtney online
7810 Design and Methodological Approaches in Biomedical Informatics Powell, Kimerly online
7891 Seminars in Biomedical Informatics Au, Kin Fai/Zhang, Ping F 11:00-12:00 pm
8150 Rigorous and Reproducible Design and Data Analysis Coombes, Kevin/Kline, David F 2:00-5:00 pm


Summer 2020
5750-10 Methods in Biomedical Informatics and Data Science Zhang, Pengyue TR 9:35-10:55 am
5750-20 Methods in Biomedical Informatics and Data Science Zhang, Yan online

Course Descriptions


BMI 5710 - Introduction to Biomedical Informatics5710_syllabus_AU18.pdf5710_syllabus_AU18.pdf
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 course work 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.

BMI 5730 - Introduction to Bioinformatics
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.

BMI 5740 - Introduction to Research Informatics

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.

BMI 5750 - Methods in Biomedical Informatics and Data Science​
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:
1) Basic computational skills (R programming)
2) Data integration (data transformation / merging / manipulation, metadata integration)
3) Basic probability (conditional probability, Bayes theorem, probability distributions, sampling distributions)
4) Study design principles (population and sample selection, study design principles)
5) Exploratory analysis of data (graphical displays of data, data summarization)
6) Statistical analysis of data (estimation, confidence intervals, hypothesis testing, regression, two-group tests, analysis of variance (ANOVA), survival analysis)
7) Power and sample size calculations
8) In silico hypothesis generation (data mining, text mining, and visualization)
9) Introduction to data and methods in bioinformatics (clustering, classification, RNA-seq data analysis)

BMI 5760 – Public Health Informatics
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 of the role and broad application of informatics to promoting health and preventing disease. ​​

  

BMI 5770 – Health Analytics

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 moving from that data to meaningful questions, explore how we frame answers to questions using available tools and then practice presenting that data in meaningful ways.


BMI 7600 - Metabolomics, Principles and Practice

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 chose 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. 

 
BMI 7810 - Design and Methodological Approaches in Biomedical Informatics
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.
 
BMI 7830 –  Integrative Methods of Bioinformatics for Human Diseases
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 both 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. 

BMI 7040 – Clinical Informatics
This course is geared specifically towards students and trainees working or taking coursework within the BMI department.The course provides training in the theories, methods and application of clinical informatics. Clinical Informatics is 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.​

BMI 7891 – Seminars in Biomedical Informatics

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).
​​​​​​​
​​​​​​​​​​​​This course is geared specifically towards students and trainees working or taking coursework within the BMI department.The course provides training in the theories, methods and application of clinical informatics. Clinical Informatics is 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.

BMI 8130 – Analysis and Applications of Genome-Scale Data

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.

​​​​​​​​​​​​​​​​​​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.​​

BMI 8140 – Measuring Patient Experiences​ and Preferences

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.​

BMI 8150 – Analysis and Applications of Genome-Scale Data

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.​​ 


Independent Studies and Research Credits

BMI 5793/8193 - Individual Studies in Biomedical Informatics

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.

BMI 7999/8999 - Research in Biomedical Informatics

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.



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