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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 | Grading: Medical Satisfactory/No Credit
Instructors: ; Musen, M. (PI)

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. May be repeated for credit. Prerequisite: background in biomedicine. Background in computer science can be helpful but not required.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Medical Satisfactory/No Credit
Instructors: ; Rubin, D. (PI); Yu, A. (TA)

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

Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisite: CS 106B; recommended: CS161; consent of instructor for 3 units.
Terms: Aut | Units: 3-4 | Grading: Medical Option (Med-Ltr-CR/NC)

BIOMEDIN 215: Data Driven Medicine

With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: CS 106A; STATS 216. Recommended: one of CS 246, STATS 305, or CS 22
Terms: Aut | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

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 | Grading: Medical Satisfactory/No Credit
Instructors: ; Altman, R. (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, Win, Spr, Sum | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

BIOMEDIN 225: Data Driven Medicine: Lectures

Lectures for BIOMEDIN 215.With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: familiarity with statistics (STATS 216) and biology.
Terms: Aut | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

BIOMEDIN 273A: A Computational Tour of the Human Genome (CS 273A, DBIO 273A)

Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing (technologies, assembly, personalized sequencing); functional landscape (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures from genomic researchers. No prerequisites. Seehttp://cs273a.stanford.edu/.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

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. 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 CS109, and basic machine learning such as CS229. No prior knowledge of genomics is necessary.
Terms: Aut | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

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 | Grading: Letter or Credit/No Credit

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 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bagley, S. (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); 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); Meng, T. (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); Kanagawa, K. (GP); Thompson, J. (GP)

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 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bagley, S. (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); 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); Meng, T. (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); Kanagawa, K. (GP); Thompson, J. (GP)

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 | Grading: Medical School MD Grades
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bagley, S. (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); 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); Meng, T. (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); Kanagawa, K. (GP); Thompson, J. (GP)

BIOMEDIN 390A: 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 | Grading: Medical Satisfactory/No Credit

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 | Grading: Medical Satisfactory/No Credit

BIOMEDIN 390C: 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 | Grading: Medical Satisfactory/No Credit

BIOMEDIN 432: Analysis of Costs, Risks, and Benefits of Health Care (HRP 392)

(Same as MGTECON 332) For graduate students. How to do cost/benefit analysis when the output is difficult or impossible to measure. How do M.B.A. analytic tools apply in health services? Literature on the principles of cost/benefit analysis applied to health care. Critical review of actual studies. Emphasis is on the art of practical application.
Terms: Aut | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)

BIOMEDIN 801: TGR Master's Project

Project credit for masters students who have completed all course requirements and minimum of 45 Stanford units.
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bagley, S. (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); 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); Meng, T. (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); Kanagawa, K. (GP); Thompson, J. (GP)

BIOMEDIN 802: TGR PhD Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bagley, S. (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); 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); Meng, T. (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); Kanagawa, K. (GP); Thompson, J. (GP)
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