Ohio State Navbar

Sign In

Biomedical Informatics Courses

Go to Autumn 2018 Courses          Go to Spring 20​19 Courses 


Autumn 2018


 
BMI 5710 - Introduction to Biomedical Informatics

Director:
 Courtney L. Hebert, MD, MS
Schedule: Tuesday/Thursday, 3:55 - 5:15 PM
Location: 10 Psychology Building
Synopsis: 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 5760 – Public Health Informatics


Director: Megan Gregory, PhD and Tasneem Motiwala, PhD
Schedule/Location: Online
Synopsis: ​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 7891 – Seminars in Biomedical Informatics

Director: Ewy Mathé, PhD
Schedule: Seminars are Fridays, 11:00 AM - 12:00 PM
Location: Varies
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, 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.


BMI 8050.01 – Machine Learning for Bioinformatics

8050.01 Machine Learning.JPGDirector: Kevin Coombes, PhD
Schedule: 
Tuesday/Wednesday, 2:20 - 3:40 PM
Location
: 245 Lincoln Tower
Synopsis: ​
This course will teach students the primary machine learning algorithms used in bioinformatics. 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 of 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 regressions, lasso, and elastics nets), deep learning (using TensorFlow), non-linear dimension reduction (including t-SNE and ISOMAP), directed and undirected graphical models, and association rules. By the end of the course, students will have had practice applying all of these methods to actual data sets.



BMI 8130 – Analysis and Applications of Genome-Scale Data

Directors: Kevin Coombes, PhD and Yan Zhang, 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.​​


 

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​. Graded S/U.



BMI 7999/8999 - Research in Biomedical Informatics

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.

 

Spring 2019

 

BMI 5730 - Introduction to Bioinformatics

Director:  Lijun Cheng, PhD
Schedule: Wednesday/Thursday: 12:45 - 2:05 PM
Location: 245 Lincoln Tower
Synopsis: 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 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 7600 - Metabolomics, Principles and Practice

Directors: Jessica Cooperstone, PhD, Rachel Kopec, PhD, Emmanuel Hatzakis, PhD, Matthias Klein, PhD, 
Devin Peterson, PhD, Ewy Mathé, PhD
Schedule: Mondays: 12:40-1:35pm, Wednesdays: 12:40-1:35pm, 1:45pm-3:45pm
Location: 334 Kottman Hall
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 7810-10 - Design and Methodological Approaches in Biomedical Informatics

7810 Design.JPGDirectors: Kim Powell, PhD
Schedule/Location: Online
Course description:
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.

Course learning outcomes:
By the end of this course, students should successfully be able to:
Interpret major funding sources’ guidelines (e.g. NIH, NSF) in order to identify avenues for potential research in Biomedical Informatics
Examine, describe, and prepare the major components of an NIH fellowship grant proposal.
Demonstrate scientific communication skills by defending an NIH fellowship grant proposal through creation of a presentation.

Better Together: Creating a Highly Engaging Online Class​

ODEE and BMI join forces to create BMI 7810!


BMI 7891 – Seminars in Biomedical Informatics

Director: Ewy Mathé, PhD
Schedule: Seminars are Fridays, 11:00 AM - 12:00 PM
Location: Rotates
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, 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.


BMI 7999/8999 - Research in Biomedical Informatics

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.


 

 

Summer 2019


BMI 5750-10 - Methods in Biomedical Informatics and Data Science​

Director: Guy Brock, PhD

Schedule: Summer Term - Tuesday/Thursday: 9:00 - 10:20 AM
Location: TBD
Synopsis: Students will gain a familiarity with methods used during the course of the design, implementation, and evaluation of Biomedical Informatics platforms, and be able to appropriately select and combine such approaches on a project-specific basis. This course will establish an application-oriented understanding of how to appropriately use such methods in order to satisfy project-specific needs and deliverables. The objectives and outcomes for this course are:
  • Demonstrate the ability to lead project teams utilizing the aforementioned methods.
  • Demonstrate the ability to critically evaluate the outcomes generated via the appropriate use of the aforementioned methods.
  • Demonstrate the ability to critically evaluate the outcomes generated via the appropriate use of the aforementioned methods.
The following items will be discussed during the summer-long course: Intro to UML Modeling, Modeling Activities & User Needs, Modeling Driven Architecture, Procedural Programming, Defining Data Types, Managing Collections of Data, Advanced Procedural Programming, Designing and Executing Database Queries, Advanced Query Operations, Web Application Design, and Accessing Databases Using Procedural Languages.​


BMI 5750-20 - Methods in Biomedical Informatics and Data Science


Director:
 Yan Zhang, PhD
Schedule/Location: 4-week Session 1, Online
Course description: 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)
Class Format: Online lectures, take-home lab exercises, quizzes and forum discussions.

This course is department permission only, please contact Education Program Manager, Gabrielle Kokanos to enroll.


 

​​​​​​​​​​​​​​​​