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Computational Medicine for Master’s Students

Computational Medicine (CM) is an emerging discipline devoted to the development of quantitative approaches for understanding the mechanisms, diagnosis and treatment of human disease through applications of mathematics, engineering and computational science. The core approach of CM is to develop computational models of the molecular biology, physiology, and anatomy of disease, and apply these models to improve patient care. CM approaches can provide insight into and across many areas of biology, including genetics, genomics, molecular networks, cellular and tissue physiology, organ systems, and whole body pharmacology. At the Institute for Computational Medicine (ICM), the “birthplace” of CM, research focuses on four key areas: Computational Molecular Medicine, Computational Physiological Medicine, Computational Anatomy, and Computational Healthcare.

Below, you will find a suggested list of courses to help you in your course planning. Your academic interests determine the remaining courses (focus area electives). You will meet with the faculty lead of your chosen focus area to determine your course plan. The program administrator will provide additional advisement and course approval. Please note that all listed courses are suggested and may not always be offered. Course offerings are subject to change from semester-to-semester.

A faculty member and a student discuss equations at a white board.
CM Focus Area Courses
  • Introduction to Computational Medicine: Imaging (EN.580.631)
  • Introduction to Computational Medicine: The Physiome (EN.580.633)
  • Foundations of Computational Biology & Bioinformatics (EN.580.688)
  • Systems Pharmacology and Personalized Medicine (EN.580.640)
  • Data Science for Public Health I (PH.140.628.71)
  • Data Science for Public Health II (PH.140.629.71)
CM Focus Area Electives
  • Advanced Topics in Genomic Data Analysis (EN.601.751)
  • Advanced Topics in Pharmacokinetics and Pharmacodynamics (EN.540.639)
  • Computational Genomics: Data Analysis (EN.601.648)
  • Computational Genomics: Sequences (EN.601.647)
  • Computational Stem Cell Biology (EN.580.647)
  • Data Mining (EN.553.636)
  • Introduction to Data Science for Biomedical Engineers (EN.580.664)
  • Introduction to Precision Medicine Analytics (EN.600.721)
  • Introduction to Probability (EN.553.620)
  • Introduction to Stochastic Processes (EN.553.426/626)
  • Machine Learning (EN.601.675)
  • Machine Learning: Data to Models (EN.601.676)
  • Machine Learning: Deep Learning (EN.601.682)
  • Mathematics of Deep Learning (EN.580.745)
  • Models of the Neuron (EN.580.639)
  • Modeling, Simulation, and Monte Carlo (EN.553.664)
  • Biomedical Data Design I and II (EN.580.697/638)
  • Practical Ethics for Future Leaders (EN 580.496)
  • Precision Care Design I (EN.580.670)
  • Precision Care Design II (EN.580.671)
  • Principles of Complex Networked Systems (EN.520.622)
  • Systems Pharmacology & Personalized Medicine (EN.580.640)

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