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1 - 10 of 17 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 3 times (up to 3 units total)

BIOMEDIN 210: Modeling Biomedical Systems (CS 270)

At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. Prerequisites: CS106A. Basic familiarity with Python programming, biology, probability, and logic are assumed.
Terms: Win | Units: 3

BIOMEDIN 217: Translational Bioinformatics (BIOE 217, CS 275, GENE 217)

Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of CS 106A and familiarity with biology and statistics.
Terms: Win | Units: 4

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 (EPI 261, STATS 261)

(Formerly HRP 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 or R. Special topics: cross-fold validation and bootstrap inference.
Terms: Win | Units: 3

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

A computational primer to "hacking" the most amazing operating system "disk" on the planet: your genome. Handling genomic data is deceptively easy. But that's muscle. You want to be the brain, too. 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 modifications behind amazing animal adaptations. The course will introduce ideas from computational genomics, machine learning and natural language processing. Course includes primers on molecular biology, and text processing languages. Prerequisites: CS106A or equivalent. No biological background assumed.
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 2 times (up to 6 units total)
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) ; 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)

BIOMEDIN 299: Directed Reading and Research

For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit
Instructors: Altman, R. (PI) ; Ashley, E. (PI) ; Baiocchi, M. (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) ; Brunet, A. (PI) ; Brutlag, D. (PI) ; Bustamante, C. (PI) ; Butte, A. (PI) ; Chang, H. (PI) ; Chen, J. (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) ; Dinakarpandian, D. (PI) ; Dror, R. (PI) ; Dumontier, M. (PI) ; Elias, J. (PI) ; Fagan, L. (PI) ; Feldman, M. (PI) ; Ferrell, J. (PI) ; Fraser, H. (PI) ; Gentles, A. (PI) ; Gerritsen, M. (PI) ; Gevaert, O. (PI) ; Goldstein, M. (PI) ; Greenleaf, W. (PI) ; Guibas, L. (PI) ; Hastie, T. (PI) ; Hernandez-Boussard, 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) ; Langlotz, C. (PI) ; Levitt, M. (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) ; Qi, S. (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) ; Tu, S. (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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