BMI Course


Course Name: Applications of Machine Learning and Artificial Intelligence in Biomedical Informatics

Course Number: BMI 8050

Classroom: Carmenzoom (online)

Time: Autumn, 2020; Monday, 2:15‐5:00 PM

Instructor: Qin Ma, Ph.D.

GitHub repository: BMI8050-2020

Introduction: 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.


Course Name: Analysis and Applications of Genome‐Scale Data

Course Number: BMI 8130

Classroom: Biological Sciences Bldg 676

Time: Autumn, 2019; Tuesday & Thursday, 9:35‐10:55 am

Instructor: Kin Fai Au and Qin Ma

TA: Tara Eicher (eicher.33@osu.edu)

TA Office Hour: 9:00 – 10:00 A.M, Monday

TA Desk: 340-09 Lincoln Tower

Introduction: The goal of this course is to introduce trainees to the fundamental algorithms and data analysis skills needed to understand and analyze genome-scale data sets, including genomics, transcriptomics, epigenetics, single-cell sequencing data and long-read sequencing data. For each data types, the course will cover the fundamental algorithms and software usage, including 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 differential expression for normal vs. tumor). (2) Class Discovery seeks to discuss the inherent structure present in a data set (such as t-SNE and principal component analysis for cell clustering based on single-cell RNA-seq). (3) Class Prediction seeks to discover and validate models that can accurately predict the class or the outcome of new samples (such as prediction of methylation status). The course will include an introduction to, and hands-on experience with, the R statistical software and Linux environment and to the use of R packages that can be applied to these kinds of problems.


Course Name: Fundamentals of Grant Writing

Course Number: BSGP 7070

Time: Autumn, 2019; Monday, 3:00‐5:30 pm

Instructor: Qin Ma

Learning Objectives:

  • Understand basic grant writing steps and procedures.
  • Define a single overall goal for your research project that is attainable.
  • Define the gap in knowledge that prevents getting to your goal.
  • Propose an objective that defines what your study will produce to fill gap and attain goal.
  • Establish a hypothesis that is linked to the objective and is your “best bet answer”.
  • Propose aims that test the hypothesis and are related to your overall goal and objective.
  • Develop a research strategy and define expected outcomes and limitations.
  • Learn to think like a reviewer.
  • Create a grant application for submission and review by your peers.