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Title: Russ B. Altman's Profile | Stanford Profiles
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Texto: Russ B. Altman's Profile | Stanford Profiles Stanford Profiles Browse Sign In Edit My Profile Russ B. Altman Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline), of Biomedical Data Science and, by courtesy, of Computer Science Web page: https://rbaltman.people.stanford.edu Print Profile Email Profile View Stanford-only Profile Tab Menu Bio Research & Scholarship Teaching Publications Bio Russ Biagio Altman is a professor of bioengineering, genetics, medicine, and biomedical data science (and of computer science, by courtesy) and past chairman of the Bioengineering Department at Stanford University. His primary research interests are in the application of computing and informatics technologies to problems relevant to medicine. He is particularly interested in methods for understanding drug action at molecular, cellular, organism and population levels. His lab studies how human genetic variation impacts drug response (e.g., http://www.pharmgkb.org/). Other work focuses on the analysis of biological molecules to understand the actions, interactions and adverse events of drugs (e.g., http://feature.stanford.edu/). He helps lead an FDA-supported Center of Excellence in Regulatory Science & Innovation.Dr. Altman holds an AB from Harvard College, and an MD from Stanford Medical School, and a PhD in Medical Information Sciences from Stanford. He received the U.S. Presidential Early Career Award for Scientists and Engineers and a National Science Foundation CAREER Award. He is a fellow of the American College of Physicians (ACP), the American College of Medical Informatics (ACMI), the American Institute of Medical and Biological Engineering (AIMBE), and the American Association for the Advancement of Science (AAAS). He is a member of the National Academy of Medicine (formerly the Institute of Medicine, IOM). He is a past-president, founding board member, and a fellow of the International Society for Computational Biology (ISCB), and a past-president of the American Society for Clinical Pharmacology & Therapeutics (ASCPT). He has chaired the Science Board advising the FDA commissioner, currently serves on the NIH Director’s Advisory Committee, and is cochair of the IOM Drug Forum. He is an organizer of the annual Pacific Symposium on Biocomputing, and a founder of Personalis, Inc. Dr. Altman is board certified in Internal Medicine and in Clinical Informatics. He received the Stanford Medical School graduate teaching award in 2000 and mentorship award in 2014. Academic Appointments Professor, Bioengineering Professor, Genetics Professor, Medicine - Biomedical Informatics Research Professor, Biomedical Data Science Professor (By courtesy), Computer Science Member, Bio-X Member, Cardiovascular Institute Member, Maternal & Child Health Research Institute (MCHRI) Member, Stanford Cancer Institute Member, Wu Tsai Neurosciences Institute Administrative Appointments Faculty Director, SPADA (Stanford Predictives & Diagnostics Accelerator) (2016 - Present) Faculty Director, 100 Year Study of Artificial Intelligence (2015 - Present) Associate Director, Human-Centered Artificial Intelligence Institute (2018 - Present) Member, Biomedical Library and Informatics Research Commitee Study Section (NIH) (2002 - 2005) President, International Society for Computational Biology (2000 - 2001) President, American Society for Clinical Pharmacology and Therapeutics (2013 - 2014) Director, Biomedical Informatics Training Program (2000 - 2018) Chairman, Department of Bioengineering (2007 - 2012) Chair, FDA Science Board (2013 - 2014) Member, Advisory Committee to the Director (ACD), NIH (2013 - 2016) Honors & Awards Fellow, American Association for the Advancement of Science (2014) Stanford Medical School Mentorship Award, Stanford Medical School (2014) Fellow, International Society for Computational Biology (2010) Member, Institute of Medicine of the National Academies (2009) Fellow, American Institute for Medical and Biological Engineering (2007) Award for Excellence in Graduate Teaching, Stanford Medical School (2000) Fellow, American College of Medical Informatics (1998) Fellow, American College of Physicians (1998) U.S. Presidential Early Career Award for Scientists & Engineers, NIH (1997) Post-Doctoral Fellowship, Howard Hughes Medical Institute (1991) Boards, Advisory Committees, Professional Organizations Co-Founder, Personalis.com (2013 - Present) Editor-in-Chief, Annual Reviews of Biomedical Data Science (2016 - Present) Board of Directors, YouScript.com (2018 - Present) Advisor, Vanderbilt University Medical School (2014 - Present) Advisor, NIH Advisory Committee to the Director (ACD) (2013 - 2017) Member, FDA Commissioner Science Board (2011 - 2014) Co-Organizer, Pacific Symposium on Biocomputing (psb.stanford.edu) (1995 - Present) Program Affiliations Symbolic Systems Program Professional Education AB, Harvard College, Biochemistry & Molecular Biology (1983) PhD, Stanford University, Medical Information Sciences (1989) MD, Stanford University, Medicine (1990) Community and International Work Host, "The Future of Everything with Russ Altman" SiriusXM 121 Topic Science & Technology Interviews Partnering Organization(s) Stanford Communications, SiriusXM Populations Served General Public Location International Ongoing Project Yes Opportunities for Student Involvement No Physician, Pharmacogenomics Consult Service , Stanford Clinics, CA Topic Providing genetic advice about drug response Partnering Organization(s) Stanford Health Care Populations Served Local population Location International Ongoing Project Yes Opportunities for Student Involvement No Patents Nicholas Tatonetti, Russ B. Altman, Guy Haskin Fernald. "United States Patent 9305267 Signal detection algorithms to identify drug effects and drug interactions", The Board of Trustees of the Leland Stanford Junior University, Apr 5, 2016 Kathleen A. Thompson, Russ B. Altman, Oliver M. Duschka. "United States Patent 6178416 Method and apparatus for knowledgebase searching", Jan 23, 2001 Ramon M. Felciano, Russ B. Altman. "United States Patent 6052730 Method for monitoring and/or modifying web browsing sessions", The Board of Trustees of the Leland Stanford Junior University, Apr 18, 2000 Contact Academic Russ.Altman@stanford.edu University - Faculty Department:&nbspBioengineering Position: Professor Department of Bioengineering Shriram Center, 443 Via Ortega, Room 209 Altman Lab, MC: 4245 Stanford,  California  94305-4145  (650) 725-3394 (office) (650) 725-0659 (office) (650) 725-3863 (fax) Alternate Contact Tiffany Murray Administrative Associate tiffany.murray@stanford.edu 650-725-0659 (office) Additional Info Mail Code: 5444 Profile: The med school faculty profile page is: http://med.stanford.edu/profiles/Russ_Altman/ Links Curriculum Vitae PDF NIH Biosketch PDF Helix Group Website Personal home page Current Research and Scholarly Interests I am interested in the application of computational technologies to problems in molecular biology of relevance to medicine. In particular, my laboratory focuses on drug response at the molecular level, working in three areas. First, we are building a comprehensive pharmacogenomics knowledge base (http://www.pharmgkb.org/) that provides access to information relating genotype to phenotype (in particular, how variation in genetics leads to variation in response to drugs). We are interested in collaboratively discovering and applying new pharmacogenomics knowledge. Second, we are interested in the analysis of three dimensional biological structures. We have methods for analyzing protein structures to recognize and annotate active sites and binding sites, particularly in the context of interactions with small molecule drugs. We are also interested in physics-based simulation of biological structures to understand how their dynamics impact their function (http://simbios.stanford.edu/). Finally, we are interested in computational methods for analyzing functional genomics information. We use natural language processing techniques for extracting and summarizing information in the literature, chemoinformatics methods for understanding small molecule function, and machine learning & data mining techniques to understand the molecular responses to drugs. 2018-19 Courses Ethics in Bioengineering BIOE 131, ETHICSOC 131X (Spr) Frontiers in Reproductive Technology CS 17SI (Spr) Introduction to Biomedical Informatics Research Methodology BIOE 212, BIOMEDIN 212, CS 272, GENE 212 (Spr) Principles of Pharmacogenomics BIOMEDIN 224, GENE 224 (Aut, Win, Spr) Representations and Algorithms for Computational Molecular Biology BIOE 214, BIOMEDIN 214, CS 274, GENE 214 (Aut) Representations and Algorithms for Molecular Biology: Lectures BIOMEDIN 216 (Aut) Independent Studies (38) Advanced Reading and Research CS 499 (Aut, Win, Spr, Sum) Advanced Reading and Research CS 499P (Aut, Win, Spr, Sum) Bioengineering Problems and Experimental Investigation BIOE 191 (Aut, Win, Spr, Sum) Biomedical Informatics Teaching Methods BIOMEDIN 290 (Aut, Win, Spr, Sum) Computer Laboratory CS 393 (Aut, Win, Spr, Sum) Curricular Practical Training CS 390A (Aut, Win, Spr, Sum) Curricular Practical Training CS 390B (Aut, Win, Spr, Sum) Curricular Practical Training CS 390C (Aut, Win, Spr, Sum) Directed Investigation BIOE 392 (Aut, Win, Spr, Sum) Directed Reading and Research BIOMEDIN 299 (Aut, Win, Spr, Sum) Directed Reading in Biophysics BIOPHYS 399 (Aut, Win, Spr, Sum) Directed Reading in Genetics GENE 299 (Aut, Win, Spr, Sum) Directed Study BIOE 391 (Aut, Win, Spr, Sum) Graduate Research BIOPHYS 300 (Aut, Win, Spr, Sum) Graduate Research GENE 399 (Aut, Win, Spr, Sum) Graduate Research IMMUNOL 399 (Aut, Win, Spr, Sum) Independent Database Project CS 395 (Aut, Win, Spr, Sum) Independent Project CS 399 (Aut, Win, Spr, Sum) Independent Project CS 399P (Aut, Win, Spr, Sum) Independent Work CS 199 (Aut, Win, Spr, Sum) Independent Work CS 199P (Aut, Win, Spr, Sum) Medical Scholars Research BIOMEDIN 370 (Aut, Win, Spr, Sum) Medical Scholars Research GENE 370 (Aut, Win, Spr, Sum) Part-Time CPT CS 390S (Aut) Part-Time CPT CS 390T (Win) Part-Time Curricular Practical Training CS 390Q (Win, Spr) Part-Time Curricular Practical Training CS 390U (Spr) Part-time Curricular Practical Training CS 390D (Aut) Part-time Curricular Practical Training CS 390P (Win, Spr) Practical Training BIOE 299B (Sum) Programming Service Project CS 192 (Aut, Win, Spr, Sum) Research PHYSICS 490 (Win, Spr) Senior Project CS 191 (Aut, Win, Spr, Sum) Special Studies in Engineering ENGR 199 (Win, Spr) Supervised Study GENE 260 (Aut, Win, Spr, Sum) Undergraduate Research GENE 199 (Aut, Win, Spr, Sum) Writing Intensive Senior Project (WIM) CS 191W (Aut, Win, Spr) Writing of Original Research for Engineers ENGR 199W (Aut, Win, Spr) Prior Year Courses 2017-18 Courses Ethics in Bioengineering BIOE 131, ETHICSOC 131X (Spr) Introduction to Biomedical Informatics Research Methodology BIOE 212, BIOMEDIN 212, CS 272, GENE 212 (Spr) Principles of Pharmacogenomics BIOMEDIN 224, GENE 224 (Aut, Win, Spr, Sum) Representations and Algorithms for Computational Molecular Biology BIOE 214, BIOMEDIN 214, CS 274, GENE 214 (Aut) Representations and Algorithms for Molecular Biology: Lectures BIOMEDIN 216 (Aut) 2016-17 Courses Ethics in Bioengineering BIOE 131 (Spr) Introduction to Biomedical Informatics Research Methodology BIOE 212, BIOMEDIN 212, CS 272, GENE 212 (Spr) Principles of Pharmacogenomics BIOMEDIN 224, GENE 224 (Aut, Win, Spr, Sum) Representations and Algorithms for Computational Molecular Biology BIOE 214, BIOMEDIN 214, CS 274, GENE 214 (Aut) Representations and Algorithms for Molecular Biology: Lectures BIOMEDIN 216 (Aut) 2015-16 Courses Ethics in Bioengineering BIOE 131 (Spr) Introduction to Biomedical Informatics Research Methodology BIOE 212, BIOMEDIN 212, CS 272, GENE 212 (Spr) Principles of Pharmacogenomics BIOMEDIN 224 (Win, Spr, Sum) Representations and Algorithms for Computational Molecular Biology BIOE 214, BIOMEDIN 214, CS 274, GENE 214 (Aut) Representations and Algorithms for Molecular Biology: Lectures BIOMEDIN 216 (Aut) Stanford Advisees Doctoral Dissertation Reader (AC) Derek Croote , Ragini Phansalkar , Kevin Raines Postdoctoral Faculty Sponsor Jake Lever , Sheng Wang Doctoral Dissertation Advisor (AC) Lichy Han , Adam Lavertu , Lu Yang Master's Program Advisor Alli Keys , Clara McCreery , Noor Siddiqui Doctoral (Program) Stephanie Schmidt , Erica Schwarz , Artem Trotsyuk Graduate and Fellowship Programs Bioengineering (Phd Program) Biomedical Informatics (Masters Program) Biomedical Informatics (Phd Program) Biophysics (Phd Program) Genetics (Phd Program) All Publications High precision protein functional site detection using 3D convolutional neural networks BIOINFORMATICS Torng, W., Altman, R. B. 2019 ; 35 (9) : 1503–12 View details for DOI 10.1093/bioinformatics/bty813 View details for Web of Science ID 000469491000009 High precision protein functional site detection using 3D convolutional neural networks. Bioinformatics (Oxford, England) Torng, W., Altman, R. B. 2019 ; 35 (9) : 1503–12 Abstract MOTIVATION: Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites.RESULTS: In this paper, we present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-based protein functional site detection. The framework can extract task-dependent features automatically from the raw atom distributions. We benchmarked our method against other methods and demonstrate better or comparable performance for site detection. Our deep 3DCNNs achieved an average recall of 0.955 at a precision threshold of 0.99 on PROSITE families, detected 98.89 and 92.88% of nitric oxide synthase and TRYPSIN-like enzyme sites in Catalytic Site Atlas, and showed good performance on challenging cases where sequence motifs are absent but a function is known to exist. Finally, we inspected the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features within protein functional sites.AVAILABILITY AND IMPLEMENTATION: The 3DCNN models described in this paper are available at https://simtk.org/projects/fscnn.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. View details for PubMedID 31051039 A global network of biomedical relationships derived from text BIOINFORMATICS Percha, B., Altman, R. B. 2018 ; 34 (15) : 2614–24 View details for DOI 10.1093/bioinformatics/bty114 View details for Web of Science ID 000440967900012 A research roadmap for next-generation sequencing informatics SCIENCE TRANSLATIONAL MEDICINE Altman, R. B., Prabhu, S., Sidow, A., Zook, J. M., Goldfeder, R., Litwack, D., Ashley, E., Asimenos, G., Bustamante, C. D., Donigan, K., Giacomini, K. M., Johansen, E., Khuri, N., Lee, E., Liang, X. S., Salit, M., Serang, O., Tezak, Z., Wall, D. P., Mansfield, E., Kass-Hout, T. 2016 ; 8 (335) Abstract Next-generation sequencing technologies are fueling a wave of new diagnostic tests. Progress on a key set of nine research challenge areas will help generate the knowledge required to advance effectively these diagnostics to the clinic. View details for DOI 10.1126/scitranslmed.aaf7314 View details for PubMedID 27099173 Data-Driven Prediction of Drug Effects and Interactions SCIENCE TRANSLATIONAL MEDICINE Tatonetti, N. P., Ye, P. P., Daneshjou, R., Altman, R. B. 2012 ; 4 (125) Abstract Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions. View details for DOI 10.1126/scitranslmed.3003377 View details for Web of Science ID 000301538300005 View details for PubMedID 22422992 View details for PubMedCentralID PMC3382018 Clinical assessment incorporating a personal genome LANCET Ashley, E. A., Butte, A. J., Wheeler, M. T., Chen, R., Klein, T. E., Dewey, F. E., Dudley, J. T., Ormond, K. E., Pavlovic, A., Morgan, A. A., Pushkarev, D., Neff, N. F., Hudgins, L., Gong, L., Hodges, L. M., Berlin, D. S., Thorn, C. F., Sangkuhl, K., Hebert, J. M., Woon, M., Sagreiya, H., Whaley, R., Knowles, J. W., Chou, M. F., Thakuria, J. V., Rosenbaum, A. M., Zaranek, A. W., Church, G. M., Greely, H. T., Quake, S. R., Altman, R. B. 2010 ; 375 (9725) : 1525-1535 Abstract The cost of genomic information has fallen steeply, but the clinical translation of genetic risk estimates remains unclear. We aimed to undertake an integrated analysis of a complete human genome in a clinical context.We assessed a patient with a family history of vascular disease and early sudden death. Clinical assessment included analysis of this patient's full genome sequence, risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome and clinical risk. Disease and risk analysis focused on prediction of genetic risk of variants associated with mendelian disease, recognised drug responses, and pathogenicity for novel variants. We queried disease-specific mutation databases and pharmacogenomics databases to identify genes and mutations with known associations with disease and drug response. We estimated post-test probabilities of disease by applying likelihood ratios derived from integration of multiple common variants to age-appropriate and sex-appropriate pre-test probabilities. We also accounted for gene-environment interactions and conditionally dependent risks.Analysis of 2.6 million single nucleotide polymorphisms and 752 copy number variations showed increased genetic risk for myocardial infarction, type 2 diabetes, and some cancers. We discovered rare variants in three genes that are clinically associated with sudden cardiac death-TMEM43, DSP, and MYBPC3. A variant in LPA was consistent with a family history of coronary artery disease. The patient had a heterozygous null mutation in CYP2C19 suggesting probable clopidogrel resistance, several variants associated with a positive response to lipid-lowering therapy, and variants in CYP4F2 and VKORC1 that suggest he migh


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