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BIOMEDIN 156: Economics of Health and Medical Care (BIOMEDIN 256, ECON 126, HRP 256)

Institutional, theoretical, and empirical analysis of the problems of health and medical care. Topics: demand for medical care and medical insurance; institutions in the health sector; economics of information applied to the market for health insurance and for health care; measurement and valuation of health; competition in health care delivery. Graduate students with research interests should take ECON 249. Prerequisites: ECON 50 and either ECON 102A or STATS 116 or the equivalent. Recommended: ECON 51.
Terms: Aut, Win | Units: 5 | UG Reqs: WAY-SI

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)
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
Instructors: ; Rubin, D. (PI)

BIOMEDIN 206: Informatics in Industry

Effective management, modeling, acquisition, and mining of biomedical information in healthcare and biotechnology companies and approaches to information management adopted by companies in this ecosystem. Guest speakers from pharmaceutical/biotechnology companies, clinics/hospitals, health communities/portals, instrumentation/software vendors. May be repeated for credit.
Terms: Spr | Units: 1 | Repeatable for credit

BIOMEDIN 208: Clinical Informatics Literature Review Seminar

Focus is on reading and discussing seminal papers in clinical and health informatics. Topics include biomedical informatics methods, systems design, implementation and evaluation. Limited enrollment.
Terms: Win | Units: 1

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

Methods for modeling biomedical systems and for making those models explicit in the context of building 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. Recommended: exposure to object-oriented systems, basic biology.
Terms: Win | Units: 3

BIOMEDIN 212: Introduction to Biomedical Informatics Research Methodology (BIOE 212, CS 272, GENE 212)

Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 211 or 214 or 217 or consent of instructor.
Terms: Spr | Units: 3
Instructors: ; Altman, R. (PI)

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, Gibbs Sampling, 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. Prerequisites: programming skills; consent of instructor for 3 units.
Terms: Aut | Units: 3-4

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; familiarity with statistics (STATS 202) and biology. Recommended: one of CS 246 (previously CS 345A), STATS 305, or CS 229.
Terms: Aut | Units: 3
Instructors: ; Shah, N. (PI)

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

BIOMEDIN 217: Translational Bioinformatics (CS 275)

Analytic, storage, and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; analysis of microarray data; analysis of polymorphisms, proteomics, and protein interactions; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of CS 106A and familiarity with statistics and biology.
Terms: Win | Units: 4

BIOMEDIN 218: Translational Bioinformatics Lectures

Same content as BIOMEDIN 217; for medical and graduate students who attend lectures and participate in limited assignments and final project. Analytic, storage, and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; analysis of microarray data; analysis of polymorphisms, proteomics, and protein interactions; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming at the level of CS 106A; familiarity with statistics and biology.
Terms: Win | Units: 2

BIOMEDIN 219: Mathematical Models and Medical Decisions

Analytic methods for determining the optimal diagnostic and therapeutic decisions for the care of individual patients and for the design of policies affecting the care of patient populations. Topics: utility theory and probability modeling, empirical methods for estimating disease prevalence, 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-contstrained clinical policies. Extensive problem sets compliment course materials. Prerequisites: introduction to calculus and basic statistics.
Terms: Win | Units: 2
Instructors: ; Higgins, M. (PI)

BIOMEDIN 225: Data Driven Medicine: Lectures

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 202) and biology.
Terms: Aut | Units: 2
Instructors: ; Shah, N. (PI)

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: Spr | Units: 3

BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252)

Methods of conducting empirical studies which use large existing medical, survey, and other databases to ask both clinical and policy questions. Econometric and statistical models used to conduct medical outcomes research. How research is conducted on medical and health economics questions when a randomized trial is impossible. Problem sets emphasize hands-on data analysis and application of methods, including re-analyses of well-known studies. Prerequisites: one or more courses in probability, and statistics or biostatistics.
Terms: Spr | Units: 4

BIOMEDIN 256: Economics of Health and Medical Care (BIOMEDIN 156, ECON 126, HRP 256)

Institutional, theoretical, and empirical analysis of the problems of health and medical care. Topics: demand for medical care and medical insurance; institutions in the health sector; economics of information applied to the market for health insurance and for health care; measurement and valuation of health; competition in health care delivery. Graduate students with research interests should take ECON 249. Prerequisites: ECON 50 and either ECON 102A or STATS 116 or the equivalent. Recommended: ECON 51.
Terms: Aut, Win | Units: 5

BIOMEDIN 258: Genomics, Bioinformatics and Medicine (BIOC 158, BIOC 258, HUMBIO 158G)

Molecular basis of inherited human disease. Diagnostics approaches: simple Mendelian diseases and complex, multifactorial diseases. Genomics: functional genomics, epigenetics, gene expression, SNPs, copy number and other structural genomic variations involved in disease. Novel therapeutic methods: stem cell therapy, gene therapy and drug developments that depend on the knowledge of genomics. Personal genomics, pharmacogenomics, clinical genomics and their role in the future of preventive medicine. Prerequisites: BIO 41 or HUMBIO 2A or consent of instructor. Those with credit in BIOC 118 not eligible to enroll. Course webpage: http://biochem158.stanford.edu/
Terms: Aut, Win, Spr | Units: 3 | Repeatable 2 times (up to 6 units total)
Instructors: ; Brutlag, D. (PI)

BIOMEDIN 260: Computational Methods for Biomedical Image Analysis and Interpretation (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 with reduced project requirements requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab highly recommended.
Terms: Spr | Units: 3-4

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); 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); Das, A. (PI); Das, R. (PI); Davis, R. (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); 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); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Riedel-Kruse, I. (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); Xing, L. (PI); Habebo, T. (GP); 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
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); Das, A. (PI); Das, R. (PI); Davis, R. (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); 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); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Riedel-Kruse, I. (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); Xing, L. (PI); Cisneros, S. (GP); Habebo, T. (GP); 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
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); Das, A. (PI); Das, R. (PI); Davis, R. (PI); Desai, M. (PI); Dev, P. (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); 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); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Riedel-Kruse, I. (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); Xing, L. (PI); Habebo, T. (GP); 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

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

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

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

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
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); Das, A. (PI); Das, R. (PI); Davis, R. (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); 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); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Riedel-Kruse, I. (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); Xing, L. (PI); Habebo, T. (GP); Kanagawa, K. (GP); Thompson, J. (GP)

BIOMEDIN 802: TGR PhD Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
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); Das, A. (PI); Das, R. (PI); Davis, R. (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); 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); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Riedel-Kruse, I. (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); Xing, L. (PI); Boyd, S. (GP); Habebo, T. (GP); Kanagawa, K. (GP); Thompson, J. (GP)

BIOMEDIN 207: Smart Health through Digital Medicine

The widespread use of Health IT, such as electronic health records, and of health applications on the part of patients and consumers, will radically alter the practice of medicine in the coming decades. This seminar, comprised of guest lectures from healthcare professionals in industry and academia, will highlight the practical challenges and successes of health IT design and transformed care delivery programs. The goal of the course is to provide an understanding of how technology designs can advance the delivery and quality of healthcare. In addition to attending lectures, students will be asked to think through a health IT solution to a care delivery problem in a short report.
| Units: 1

BIOMEDIN 224: Principles of Pharmacogenomics (GENE 224)

Introduction to the relevant pharmacology, genomics, experimental methods for high-throughput measurements (sequencing, expression, genotyping), analysis methods for GWAS, chemoinformatics, and natural language processing. Review of key gene classes (cytochromes, transporters, GPCRs), key drugs for which genetics is critical (warfarin, clopidogrel, statins, NSAIDs, neuropsychiatric drugs and cancer drugs). Also reviews resources for pharmacogenomics (PharmGKB, Drugbank, CMAP, and others) as well as issues in doing clinical implementation of pharmacogenomics testing. Reading of key papers, including student presentations of this work. Problem sets; final project selected with approval of instructor. Prerequisites: two of BIO 41, BIO 42, BIO 43, BIO 44X, BIO 44Y or consent of instructor.
| Units: 3

BIOMEDIN 231: Computational Molecular Biology

Practical, hands-on approach to field of computational molecular biology. Recommended for molecular biologists and computer scientists desiring to understand the major issues concerning analysis of genomes, sequences and structures. Various existing methods critically described and strengths and limitations of each. Practical assignments utilizing tools described. Prerequisite: BIO 41 or consent of instructor. All homework and coursework submitted electronically. Course webpage: http://biochem218.stanford.edu/.
| Units: 3

BIOMEDIN 262: Computational Genomics (CS 262)

Applications of computer science to genomics, and concepts in genomics from a computer science point of view. Topics: dynamic programming, sequence alignments, hidden Markov models, Gibbs sampling, and probabilistic context-free grammars. Applications of these tools to sequence analysis: comparative genomics, DNA sequencing and assembly, genomic annotation of repeats, genes, and regulatory sequences, microarrays and gene expression, phylogeny and molecular evolution, and RNA structure. Prerequisites: 161 or familiarity with basic algorithmic concepts. Recommended: basic knowledge of genetics.
| Units: 3

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. See http://cs273a.stanford.edu/.
| Units: 3

BIOMEDIN 371: Computational Biology in Four Dimensions (BIOPHYS 371, CME 371, CS 371)

Computational approaches to understanding the three-dimensional spatial organization of biological systems and how that organization evolves over time. The course will cover cutting-edge research in both physics-based simulation and computational analysis of experimental data, at scales ranging from individual molecules to entire cells. Prerequisite: CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
| Units: 3

BIOMEDIN 374: Algorithms in Biology (CS 374)

Algorithms and computational models applied to molecular biology and genetics. Topics vary annually. Possible topics include biological sequence comparison, annotation of genes and other functional elements, molecular evolution, genome rearrangements, microarrays and gene regulation, protein folding and classification, molecular docking, RNA secondary structure, DNA computing, and self-assembly. May be repeated for credit. Prerequisites: 161, 262 or 274, or BIOCHEM 218, or equivalents.
| Units: 2-3
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