Course Descriptions

Biomedin 205 Precision Practice with Big Data (
(1 unit – may be repeated once for total of 2 units)

The other 2-4 unit core IDDM units can be selected from any of the classes which the BMI graduate students take to fulfill their degree requirements.

(6 units are required with an Application, 12 units are required without an Application)                     


The list of elective courses included is not all-inclusive. The student, advisor/mentor, and co-directors will design a program tailored to the student's interests, goals and background. .

  • BIOMEDIN 202 Introductory Biomedical Informatics. This is a survey of fundamental and current topics in biomedical informatics and is meant to give an overview of current research problems in biomedical informatics and computational approaches that address those problems. Topics include medical security and privacy, electronic medical records, controlled terminologies and biomedical ontologies, electronic retrieval, technology-assisted learning environments, medical decision-making and decision support, sequence analysis, phylogenetics, biological networks and pathways, microarray analysis, natural language processing, and protein structural analysis and prediction. Enrollment limited to medical students or with the permission of the instructor (1 unit; on line).

  • BIOMEDIN 210 Introduction - Fundamental Methods. Introduction to Biomedical Informatics: Fundamental Methods (Same as Computer Science 270) - Issues in the modeling, design, and implementation of computational systems for use in biomedicine. Topics: basic knowledge representation, controlled terminologies in medicine and biological science, fundamental algorithms, information dissemination and retrieval, knowledge acquisition, and ontologies. Emphasis is on the principles of modeling data and knowledge in biomedicine and on translation of resulting models into useful automated systems (3 units).

  • BIOMEDIN 215 Data Driven Medicine - With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: CS 106A; familiarity with statistics (STATS 202) and biology. Recommended: one of CS 246 (previously CS 345A), STATS 305, or CS 229 (3 units).

  • BIOMEDIN 260 Computational Methods for Biomedical Image Analysis and Interpretation (Same as Rad 260). Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science (2 or 4 units).

  • BIOMEDIN 217 Translational Bioinformatics—(Same as CS 275) - Analytic, storage, and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; analysis of microarray data; analysis of polymorphisms, proteomics, and protein interactions; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of CS 106A and familiarity with statistics and biology.
  • ANES 212 Machine Learning for Healthcare Quality: Precision Medicine Al Design Lab. This course provides a hands-on introduction to building machine learning systems for healthcare quality analysis and improvement. We explore several unconditional topics, including data representation, data manipulation, data analysis and data visualization. Students will be introduced to these topics during lectures. The course also provides students with a significant opportunity to investigate the application of these ideas to real-world clinical quality improvement challenges. Working with clinical mentors from the Stanford University School of Medicine students will be expected to supplement machine learning theory with a quarter-long project targeting representative clinical quality improvement challenges. Students will be encouraged to think creatively about traditionally hard quality problems and requires to perform group research exposing them to designing practical machine learning systems for healthcare.

For a more complete description of classes we encourage you to visit the Stanford University Bulletin:

In addition, for original research:

Demonstration of proficiency in programming:

  • CS 106A Programming Methodology  Discussion Group
  • CS 106B Programming Abstractions  Discussion Group
  • CS 106X* Abstractions, Methodology (accelerated) Discussion Group
  • * may substitute for CS106A and B


Completion of the project class:

  • BIOMEDIN 212 (prerequisite BMI 366)