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1 - 10 of 18 results for: BIOMEDIN

BIOMEDIN 201: Biomedical Informatics Student Seminar (BIODS 201)

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. Summer Quarter consists of critical review of relevant literature led by faculty associated with the Biomedical Informatics Program. May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable 3 times (up to 3 units total)

BIOMEDIN 205: Precision Practice with Big Data

Primarily for M.D. students; open to other graduate students. Provides an overview of how to leverage large amounts of clinical, molecular, and imaging data within hospitals and in cyberspace--big data--to practice medicine more effectively. Lectures by physicians, researchers, and industry leaders survey how the major methods of informatics can help physicians leverage big data to profile disease, to personalize treatment to patients, to predict treatment response, to discover new knowledge, and to challenge established medical dogma and the current paradigm of clinical decision-making based solely on published knowledge and individual physician experience. Prerequisite: background in biomedicine. Background in computer science can be helpful but not required.
Terms: Aut | Units: 1

BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214)

BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology ( BIOE 214, CS 274, GENE 214)Topics: This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, BLAST, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D structure, knowledge controlled terminologies for molecular function, expression analysis, chemoinformatics, pharmacogenetics, network biology. Lectures are supplemented with assignments and programming projects, which allow students to implement important computational biology algorithms. Firm prerequisite: CS 106B. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Aut | Units: 3-4

BIOMEDIN 215: Data Science for Medicine

The widespread adoption of electronic health records (EHRs) has created a new source of big data namely, the record of routine clinical practice as a by-product of care. This graduate class will teach you how to use EHRs and other patient data to discover new clinical knowledge and improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of research questions and the study designs used to address them, describe common healthcare data sources and their relative advantages and limitations, extract and transform various kinds of clinical data to create analysis-ready datasets, design and execute an analysis of a clinical dataset based on your familiarity with the workings, applicability, and limitations of common statistical methods, evaluate and criticize published research using your knowledge of 1-4 to generate new research ideas and separate hype from reality. Prerequisites: CS 106A or equivalent, STATS 60 or equivalent. Recommended: STATS 216, CS 145, STATS 305NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Aut | Units: 3

BIOMEDIN 216: Representations and Algorithms for Molecular Biology: Lectures

Lecture component of BIOMEDIN 214. One unit for medical and graduate students who attend lectures only; may be taken for 2 units with participation in limited assignments and final project. Lectures also available via internet. Prerequisite: familiarity with biology recommended.
Terms: Aut | Units: 1-2
Instructors: Altman, R. (PI)

BIOMEDIN 221: Machine Learning Approaches for Data Fusion in Biomedicine (BIODS 221)

Vast amounts of biomedical data are now routinely available for patients, raging from genomic data, to radiographic images and electronic health records. AI and machine learning are increasingly used to enable pattern discover to link such data for improvements in patient diagnosis, prognosis and tailoring treatment response. Yet, few studies focus on how to link different types of biomedical data in synergistic ways, and to develop data fusion approaches for improved biomedical decision support. This course will describe approaches for multi-omics, multi-modal and multi-scale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent, Stats 60 or equivalent.
Terms: Aut | Units: 2

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

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 | Units: 2-3

BIOMEDIN 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOPHYS 279, CME 279, CS 279)

Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background ( CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut | Units: 3

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 12 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) ; 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) ; 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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