BIOMEDIN 201: Biomedical Data Science Student Seminar (BIODS 201)
Participants report on their research projects or share a review of recent articles from biomedical data science literature. The goals are (1) to practice oral communications and effective presentations to an audience not familiar with their particular research area, and (2) to learn more about other research across biomedical data science that may be relevant to their work.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
3 times
(up to 3 units total)
Instructors:
Alsentzer, E. (PI)
;
Daneshjou, R. (PI)
;
Engelhardt, B. (PI)
...
more instructors for BIOMEDIN 201 »
Instructors:
Alsentzer, E. (PI)
;
Daneshjou, R. (PI)
;
Engelhardt, B. (PI)
;
Lu, Y. (PI)
;
Matthys, K. (PI)
;
Tian, L. (PI)
;
Wall, D. (PI)
;
Wong, W. (PI)
BIOMEDIN 206: Informatics in Industry
Effective management, modeling, acquisition, and mining of biomedical information in healthcare and biotechnology companies and approaches to information management adopted by companies in this ecosystem. Guest speakers from pharmaceutical/biotechnology companies, clinics/hospitals, health communities/portals, instrumentation/software vendors. May be repeated for credit.
Terms: Spr
| Units: 1
| Repeatable
12 times
(up to 12 units total)
Instructors:
Montgomery, S. (PI)
BIOMEDIN 208: Applied Clinical Informatics Seminar
Weekly seminar series in which seminal literature and current publications in the field of clinical informatics are reviewed and discussed. Organized by the Stanford Clinical Informatics fellowship program. Topics include electronic health record design, implementation, and evaluation; patient engagement; provider satisfaction; and hot topics in clinical informatics. Limited enrollment.
Terms: Spr
| Units: 1
| Repeatable
2 times
(up to 2 units total)
Instructors:
Li, R. (PI)
;
Morse, K. (PI)
BIOMEDIN 212: Introduction to Biomedical Informatics Research Methodology (BIOE 212, CS 272, GENE 212)
Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites:
BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Spr
| Units: 3-5
Instructors:
Altman, R. (PI)
;
Pi, S. (TA)
BIOMEDIN 217: Translational Bioinformatics (BIOE 217, CS 275, GENE 217)
Analytic 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; 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.
Terms: Spr
| Units: 3-4
BIOMEDIN 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222)
Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.
Terms: Spr
| Units: 3
BIOMEDIN 223: Deploying and Evaluating Fair AI in Healthcare (CSRE 323, EPI 220)
AI applications are proliferating throughout the healthcare system and stakeholders are faced with the opportunities and challenges of deploying these quickly evolving technologies. This course teaches the principles of AI evaluations in healthcare, provides a framework for deployment of AI in the healthcare system, reviews the regulatory environment, and discusses fundamental components used to evaluate the downstream effects of AI healthcare solutions, including biases and fairness. Prerequisites:
CS106A; familiarity with Statistics (
STATS 202), BIOMED 215, or
BIODS 220
Terms: Spr
| Units: 2-3
Instructors:
Hernandez-Boussard, T. (PI)
BIOMEDIN 224: Principles of Pharmacogenomics (GENE 224)
This course is an introduction to pharmacogenomics, including the relevant pharmacology, genomics, experimental methods (sequencing, expression, genotyping), data analysis methods and bioinformatics. The course reviews key gene classes (e.g., cytochromes, transporters) and key drugs (e.g., warfarin, clopidogrel, statins, cancer drugs) in the field. Resources for pharmacogenomics (e.g., PharmGKB, Drugbank, NCBI resources) are reviewed, as well as issues implementing pharmacogenomics testing in the clinical setting. Reading of key papers, including student presentations of this work; problem sets; final project selected with approval of instructor. Prerequisites: two of
BIO 41, 42, 43, 44X, 44Y or consent of instructor.
Terms: Aut, Spr
| Units: 3
Instructors:
Altman, R. (PI)
;
Carrillo, M. (PI)
BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252)
This course introduces and develops methods for conducting empirical research that address clinical and policy questions that are not suitable for randomized trials. Conceptual and applied models of causal inference guide the design of empirical research. Econometric and statistical models are used to conduct health outcomes research which use large existing medical, survey, and other databases Problem sets emphasize hands-on data analysis and application of methods, including re-analyses of well-known studies. This is a project-based course designed for students pursuing research training. Prerequisites: one or more courses in probability, and statistics or biostatistics.
Terms: Spr
| Units: 4
Instructors:
Bendavid, E. (PI)
BIOMEDIN 260: Computational Methods for Biomedical Image Analysis and Interpretation (BMP 260, CS 235, RAD 260)
The latest biological and medical imaging modalities and their applications in research and medicine. 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. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of
CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.
Terms: Spr
| Units: 3-4
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