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21 - 30 of 34 results for: BIOMEDIN ; Currently searching offered courses. You can also include unoffered courses

BIOMEDIN 260: Computational Methods for Biomedical Image Analysis and Interpretation (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

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 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Stude more »
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.
Terms: Aut | Units: 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 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)

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) ; 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) ; Dror, R. (PI) ; Dumontier, M. (PI) ; Elias, J. (PI) ; Fagan, L. (PI) ; Feldman, M. (PI) ; Ferrell, J. (PI) ; Fraser, H. (PI) ; Gambhir, S. (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) ; 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)

BIOMEDIN 370: Medical Scholars Research

Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum | Units: 4-18 | 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) ; 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) ; 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) ; 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) ; Tu, S. (PI) ; Utz, P. (PI) ; Walker, M. (PI) ; Wall, D. (PI) ; Winograd, T. (PI) ; Wong, W. (PI) ; Xing, L. (PI) ; Zou, J. (PI)

BIOMEDIN 390A: Curricular Practical Training

Terms: Aut, Win, Spr, Sum | Units: 1

BIOMEDIN 390B: Curricular Practical Training

Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any follow-up on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum | Units: 1
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