Biomedical Informatics (BMIN-GA)

The course is meant to provide students with an overview of and practical experience with bioinformatics techniques.

Clinical decision support can be thought of as the use of information technology to support medical decision-making. In this course, students will learn the fundamentals of clinical decision support with the end result being the design of their own clinical decision support tool.

This course will give an introduction of proteomics and mass spectrometry workflows, experimental design, and data analysis with a focus on algorithms for extracting information from experimental data.

This course provides practical training in informatics methods for analysis of next-generation DNA sequencing (NGS) data.

This is an elective course for graduate students enrolled in the Biomedical Informatics program. Students will participate in BPIC consultations, prepare reports, present consultations to faculty mentors and their peers. The students will meet weekly to discuss former consultations as case studies, ongoing consultation and strategies for effective informatics consulting.

Students will learn the most common deep learning methods emerging in medicine. Students will be able to differentiate various deep learning methods and choose the most appropriate ones for specific research methods.

The course aims to give data scientists the multidisciplinary skills to evaluate systems and apply findings to generate change that improves organizational and system level outcomes.

This course will focus on the analysis and integration of public and in-house proteogenomic data for mismatch repair (MMR) deficient endometrial tumors to identify biomarkers associated with recurrence and response to immune checkpoint therapy.