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1 - 10 of 18 results for: BIOMEDIN ; Currently searching winter courses. You can expand your search to include all quarters

BIOMEDIN 201: Biomedical Informatics Student Seminar

Participants report on recent articles from the Biomedical Informatics literature or their research projects. Goals are to teach critical reading of scientific papers and presentation skills. May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit

BIOMEDIN 208: Clinical Informatics Literature Review 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: Win | Units: 1

BIOMEDIN 210: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (CS 270)

Methods for modeling biomedical systems and for building model-based software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Students learn about current trends in the development of advanced biomedical software systems and acquire hands-on experience with several systems and tools. Prerequisites: CS106A, basic familiarity with biology, probability, and logic.
Terms: Win | Units: 3

BIOMEDIN 219: Mathematical Models and Medical Decisions

Analytic methods for determining optimal diagnostic and therapeutic decisions with applications to the care of individual patients and the design of policies applied to patient populations. Topics include: utility theory and probability modeling, empirical methods for disease prevalence estimation, probability models for periodic processes, binary decision-making techniques, Markov models of dynamic disease state problems, utility assessment techniques, parametric utility models, utility models for multidimensional outcomes, analysis of time-varying clinical outcomes, and the design of cost-constrained clinical policies. Extensive problem sets compliment the lectures. Prerequisites: introduction to calculus and basic statistics.
Terms: Win | Units: 3

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, Win, Spr, Sum | Units: 3

BIOMEDIN 226: Digital Health Practicum in a Health Care Delivery System

Practical experience implementing clinical informatics solutions with a focus on digital health in one of the largest healthcare delivery systems in the United States. Individual meetings with senior clinical informatics leaders to discuss elements of successful projects. Implementation opportunities include supporting the use of electronic health records, engagement of patients and providers via a personal health record, use of informatics to support patient service centers, and improvement of patient access to clinical data. Consent of course instructors required at least one quarter prior to student enrollment in course.
Terms: Aut, Win, Spr, Sum | Units: 2-3

BIOMEDIN 233: Intermediate Biostatistics: Analysis of Discrete Data (HRP 261, STATS 261)

Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference.
Terms: Win | Units: 3
Instructors: Sainani, K. (PI)

BIOMEDIN 245: Statistical and Machine Learning Methods for Genomics (BIO 268, CS 373, GENE 245, STATS 345)

Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Instruction includes lectures and discussion of readings from primary literature. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets.
Terms: Win | Units: 3

BIOMEDIN 273A: The Human Genome Source Code (CS 273A, DBIO 273A)

A computational introduction to the most amazing programming language on the planet: your genome. Topics include genome sequencing (assembling source code from code fragments); the human genome functional landscape: variable assignments (genes), control-flow logic (gene regulation) and run-time stack (epigenomics); human disease and personalized genomics (as a hunt for bugs in the human code); genome editing (code injection) to cure the incurable; and the source code behind amazing animal adaptations. Algorithmic approaches will introduce ideas from computational genomics, machine learning and natural language processing. Course includes primers on molecular biology, and text processing languages. Prerequisites: CS106B or equivalent.
Terms: Win | Units: 3
Instructors: Bejerano, G. (PI)

BIOMEDIN 290: Biomedical Informatics Teaching Methods

Hands-on training in biomedical informatics pedagogy. Practical experience in pedagogical approaches, variously including didactic, inquiry, project, team, case, field, and/or problem-based approaches. Students create course content, including lectures, exercises, and assessments, and evaluate learning activities and outcomes. Prerequisite: instructor consent.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Altman, R. (PI) ; Ashley, E. (PI) ; Bassik, M. (PI) ; Batzoglou, S. (PI) ; Bayati, M. (PI) ; Bejerano, G. (PI) ; Bhattacharya, J. (PI) ; Blish, C. (PI) ; Boahen, K. (PI) ; Brandeau, M. (PI) ; Bustamante, C. (PI) ; Butte, A. (PI) ; Chang, H. (PI) ; Cherry, J. (PI) ; Cohen, S. (PI) ; Covert, M. (PI) ; Curtis, C. (PI) ; Das, A. (PI) ; Das, R. (PI) ; Davis, R. (PI) ; Delp, S. (PI) ; Desai, M. (PI) ; Dill, D. (PI) ; Dumontier, M. (PI) ; Elias, J. (PI) ; Fagan, L. (PI) ; Feldman, M. (PI) ; Ferrell, J. (PI) ; Fraser, H. (PI) ; Gambhir, S. (PI) ; Gerritsen, M. (PI) ; Gevaert, O. (PI) ; Goldstein, M. (PI) ; Greenleaf, W. (PI) ; Guibas, L. (PI) ; Hastie, T. (PI) ; Hlatky, M. (PI) ; Holmes, S. (PI) ; Ji, H. (PI) ; Karp, P. (PI) ; Khatri, P. (PI) ; Kim, S. (PI) ; Kirkegaard, K. (PI) ; Klein, T. (PI) ; Koller, D. (PI) ; Krummel, T. (PI) ; Kundaje, A. (PI) ; Levitt, M. (PI) ; Levitt, R. (PI) ; Li, J. (PI) ; Longhurst, C. (PI) ; Lowe, H. (PI) ; Mallick, P. (PI) ; Manning, C. (PI) ; McAdams, H. (PI) ; Menon, V. (PI) ; Montgomery, S. (PI) ; Musen, M. (PI) ; Napel, S. (PI) ; Nolan, G. (PI) ; Olshen, R. (PI) ; Owen, A. (PI) ; Owens, D. (PI) ; Paik, D. (PI) ; Palacios, J. (PI) ; Pande, V. (PI) ; Petrov, D. (PI) ; Plevritis, S. (PI) ; Poldrack, R. (PI) ; Pritchard, J. (PI) ; Relman, D. (PI) ; Riedel-Kruse, I. (PI) ; Rivas, M. (PI) ; Rubin, D. (PI) ; Sabatti, C. (PI) ; Salzman, J. (PI) ; Shachter, R. (PI) ; Shafer, R. (PI) ; Shah, N. (PI) ; Sherlock, G. (PI) ; Sidow, A. (PI) ; Snyder, M. (PI) ; Tang, H. (PI) ; Taylor, C. (PI) ; Theriot, J. (PI) ; Tibshirani, R. (PI) ; Utz, P. (PI) ; Walker, M. (PI) ; Wall, D. (PI) ; Winograd, T. (PI) ; Wong, W. (PI) ; Xing, L. (PI) ; Zou, J. (PI)
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