ACM-BCB 2016 the 7Th ACM Conference on Bioinformatics
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A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society1 Barbara J
A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society1 Barbara J. Grosz, Harvard University, Inaugural Chair of the AI100 Standing Committee Peter Stone, University of Texas at Austin, Chair of the Inaugural AI100 Study Panel The Stanford One Hundred Year Study on Artificial Intelligence, a project that launched in December 2014, is designed to be a century-long periodic assessment of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society. Colloquially referred to as "AI100", the project issued its first report in September 2016. A Standing Committee works with the Stanford Faculty Director of AI100 in overseeing the project and designing its activities. A little more than two years after the first report appeared, we reflect on the decisions made in shaping it, the process that produced it, its major conclusions, and reactions subsequent to its release. The inaugural AI100 report [4], which is titled “Artificial Intelligence and Life in 2030,” examines eight domains of human activity in which AI technologies are already starting to affect urban life. In scope, it encompasses domains with emerging products enabled by AI methods and ones raising concerns about technological impact generated by potential AI-enabled systems. The Study Panel members who authored the report and the AI100 Standing Committee, which is the body that directs the AI100 project, intend for it to act as a catalyst, spurring conversations on how we as a society might shape and share the potentially powerful technologies that AI could enable. In addition to influencing researchers and guiding decisions in industry and governments, the report aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential. -
120421-24Recombschedule FINAL.Xlsx
Friday 20 April 18:00 20:00 REGISTRATION OPENS in Fira Palace 20:00 21:30 WELCOME RECEPTION in CaixaForum (access map) Saturday 21 April 8:00 8:50 REGISTRATION 8:50 9:00 Opening Remarks (Roderic GUIGÓ and Benny CHOR) Session 1. Chair: Roderic GUIGÓ (CRG, Barcelona ES) 9:00 10:00 Richard DURBIN The Wellcome Trust Sanger Institute, Hinxton UK "Computational analysis of population genome sequencing data" 10:00 10:20 44 Yaw-Ling Lin, Charles Ward and Steven Skiena Synthetic Sequence Design for Signal Location Search 10:20 10:40 62 Kai Song, Jie Ren, Zhiyuan Zhai, Xuemei Liu, Minghua Deng and Fengzhu Sun Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads 10:40 11:00 178 Yang Li, Hong-Mei Li, Paul Burns, Mark Borodovsky, Gene Robinson and Jian Ma TrueSight: Self-training Algorithm for Splice Junction Detection using RNA-seq 11:00 11:30 coffee break Session 2. Chair: Bonnie BERGER (MIT, Cambrige US) 11:30 11:50 139 Son Pham, Dmitry Antipov, Alexander Sirotkin, Glenn Tesler, Pavel Pevzner and Max Alekseyev PATH-SETS: A Novel Approach for Comprehensive Utilization of Mate-Pairs in Genome Assembly 11:50 12:10 171 Yan Huang, Yin Hu and Jinze Liu A Robust Method for Transcript Quantification with RNA-seq Data 12:10 12:30 120 Zhanyong Wang, Farhad Hormozdiari, Wen-Yun Yang, Eran Halperin and Eleazar Eskin CNVeM: Copy Number Variation detection Using Uncertainty of Read Mapping 12:30 12:50 205 Dmitri Pervouchine Evidence for widespread association of mammalian splicing and conserved long range RNA structures 12:50 13:10 169 Melissa Gymrek, David Golan, Saharon Rosset and Yaniv Erlich lobSTR: A Novel Pipeline for Short Tandem Repeats Profiling in Personal Genomes 13:10 13:30 217 Rory Stark Differential oestrogen receptor binding is associated with clinical outcome in breast cancer 13:30 15:00 lunch break Session 3. -
Python for Bioinformatics, Second Edition
PYTHON FOR BIOINFORMATICS SECOND EDITION CHAPMAN & HALL/CRC Mathematical and Computational Biology Series Aims and scope: This series aims to capture new developments and summarize what is known over the entire spectrum of mathematical and computational biology and medicine. It seeks to encourage the integration of mathematical, statistical, and computational methods into biology by publishing a broad range of textbooks, reference works, and handbooks. The titles included in the series are meant to appeal to students, researchers, and professionals in the mathematical, statistical and computational sciences, fundamental biology and bioengineering, as well as interdisciplinary researchers involved in the field. The inclusion of concrete examples and applications, and programming techniques and examples, is highly encouraged. Series Editors N. F. Britton Department of Mathematical Sciences University of Bath Xihong Lin Department of Biostatistics Harvard University Nicola Mulder University of Cape Town South Africa Maria Victoria Schneider European Bioinformatics Institute Mona Singh Department of Computer Science Princeton University Anna Tramontano Department of Physics University of Rome La Sapienza Proposals for the series should be submitted to one of the series editors above or directly to: CRC Press, Taylor & Francis Group 3 Park Square, Milton Park Abingdon, Oxfordshire OX14 4RN UK Published Titles An Introduction to Systems Biology: Statistical Methods for QTL Mapping Design Principles of Biological Circuits Zehua Chen Uri Alon -
“Reflections on the Status and Future of Artificial Intelligence”
STATEMENT OF: ERIC HORVITZ TECHNICAL FELLOW AND DIRECTOR MICROSOFT RESEARCH—REDMOND LAB MICROSOFT CORPORATION BEFORE THE COMMITTEE ON COMMERCE SUBCOMMITTEE ON SPACE, SCIENCE, AND COMPETITIVENESS UNITED STATES SENATE HEARING ON THE DAWN OF ARTIFICIAL INTELLIGENCE NOVEMBER 30, 2016 “Reflections on the Status and Future of Artificial Intelligence” NOVEMBER 30, 2016 1 Chairman Cruz, Ranking Member Peters, and Members of the Subcommittee, my name is Eric Horvitz, and I am a Technical Fellow and Director of Microsoft’s Research Lab in Redmond, Washington. While I am also serving as Co-Chair of a new organization, the Partnership on Artificial Intelligence, I am speaking today in my role at Microsoft. We appreciate being asked to testify about AI and are committed to working collaboratively with you and other policymakers so that the potential of AI to benefit our country, and to people and society more broadly can be fully realized. With my testimony, I will first offer a historical perspective of AI, a definition of AI and discuss the inflection point the discipline is currently facing. Second, I will highlight key opportunities using examples in the healthcare and transportation industries. Third, I will identify the important research direction many are taking with AI. Next, I will attempt to identify some of the challenges related to AI and offer my thoughts on how best to address them. Finally, I will offer several recommendations. What is Artificial Intelligence? Artificial intelligence (AI) refers to a set of computer science disciplines aimed at the scientific understanding of the mechanisms underlying thought and intelligent behavior and the embodiment of these principles in machines that can deliver value to people and society. -
Curriculum Vitae
6/14/21 CURRICULUM VITAE Edward Hance Shortliffe, MD, PhD, MACP, FACMI, FIAHSI [work] Chair Emeritus and Adjunct Professor, Department of Biomedical Informatics Vagelos College of Physicians and Surgeons, Columbia University in the City of New York [email protected] – https://www.dbmi.columbia.edu/people/edward-shortliffe/ Adjunct Professor of Biomedical Informatics College of Health Solutions Arizona State University, Phoenix, AZ [email protected] – https://isearch.asu.edu/profile/1098580 Adjunct Professor, Department of Healthcare Policy and Research (Health Informatics) Weill Cornell Medical College, New York, NY http://hpr.weill.cornell.edu/divisions/health_informatics/ [home] 272 W 107th St #5B, New York, NY 10025-7833 Phone: 212-666-8440 — Mobile: 917-640-0933 [email protected] – http://www.shortliffe.net Born: Edmonton, Alberta, Canada Date of birth: 28 August 1947 Citizenship: U.S.A. (naturalized - 1962) Spouse: Vimla L. Patel, PhD Education From To School/Institution Major Subject, Degree, and Date 9/62 6/65 The Loomis School, Windsor, CT. High School 9/65 7/66 Gresham's School, Holt, Norfolk, U.K. Foreign Exchange Student 9/66 6/70 Harvard College, Cambridge, MA. Applied Math and Computer Science, A.B., June 1970 9/70 1/75 Stanford University, Stanford, CA PhD, Medical Information Sciences, January 1975 9/70 6/76 Stanford University School of Medicine MD, June 1976. 7/76 6/77 Massachusetts General Hospital, Boston, MA Internship in Internal Medicine 7/77 6/79 Stanford University Hospital, Stanford, CA Residency in Internal Medicine Honors Graduation Magna Cum Laude, Harvard College, June 1970 Medical Scientist Training Program (MSTP), NIH-funded Stanford Traineeship, September 1971 - June 1976 Grace Murray Hopper Award (Distinguished computer scientist under age 30), Association for Computing Machinery, October 1976 Research Career Development Award, National Library of Medicine, July 1979—June 1984 Henry J. -
ISCB's Initial Reaction to the New England Journal of Medicine
MESSAGE FROM ISCB ISCB’s Initial Reaction to The New England Journal of Medicine Editorial on Data Sharing Bonnie Berger, Terry Gaasterland, Thomas Lengauer, Christine Orengo, Bruno Gaeta, Scott Markel, Alfonso Valencia* International Society for Computational Biology, Inc. (ISCB) * [email protected] The recent editorial by Drs. Longo and Drazen in The New England Journal of Medicine (NEJM) [1] has stirred up quite a bit of controversy. As Executive Officers of the International Society of Computational Biology, Inc. (ISCB), we express our deep concern about the restric- tive and potentially damaging opinions voiced in this editorial, and while ISCB works to write a detailed response, we felt it necessary to promptly address the editorial with this reaction. While some of the concerns voiced by the authors of the editorial are worth considering, large parts of the statement purport an obsolete view of hegemony over data that is neither in line with today’s spirit of open access nor furthering an atmosphere in which the potential of data can be fully realized. ISCB acknowledges that the additional comment on the editorial [2] eases some of the polemics, but unfortunately it does so without addressing some of the core issues. We still feel, however, that we need to contrast the opinion voiced in the editorial with what we consider the axioms of our scientific society, statements that lead into a fruitful future of data-driven science: • Data produced with public money should be public in benefit of the science and society • Restrictions to the use of public data hamper science and slow progress OPEN ACCESS • Open data is the best way to combat fraud and misinterpretations Citation: Berger B, Gaasterland T, Lengauer T, Orengo C, Gaeta B, Markel S, et al. -
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UC San Diego UC San Diego Electronic Theses and Dissertations Title Predicting growth optimization strategies with metabolic/expression models Permalink https://escholarship.org/uc/item/6nr2539t Author Liu, Joanne Publication Date 2017 Supplemental Material https://escholarship.org/uc/item/6nr2539t#supplemental Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Predicting growth optimization strategies with metabolic/expression models A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioinformatics and Systems Biology by Joanne K. Liu Committee in charge: Professor Karsten Zengler, Chair Professor Nathan Lewis, Co-Chair Professor Michael Burkart Professor Terry Gaasterland Professor Bernhard Palsson Professor Milton Saier 2017 Copyright Joanne K. Liu, 2017 All rights reserved. The dissertation of Joanne K. Liu is approved, and it is acceptable in quality and form for publication on micro- film and electronically: Co-Chair Chair University of California, San Diego 2017 iii DEDICATION To my mom and dad, who I cannot thank enough for supporting me throughout my education, and to The One. iv EPIGRAPH Essentially, all models are wrong, but some are useful. |George E. P. Box v TABLE OF CONTENTS Signature Page.................................. iii Dedication..................................... iv Epigraph.....................................v Table of Contents................................ -
Machine Learning, Reasoning, and Intelligence in Daily Life: Directions and Challenges
Machine Learning, Reasoning, and Intelligence in Daily Life: Directions and Challenges Eric Horvitz Microsoft Research Redmond, Washington USA 98052 [email protected] Abstract An example of an implicit integration of ambient learning Technical developments and trends are providing a and reasoning is the effort by our team to create a probabil- fertile substrate for creating and integrating ma- istic action prediction and prefetching subsystem that is em- chine learning and reasoning into multiple applica- bedded deeply in the kernel of Microsoft’s Windows Vista tions and services. I will review several illustrative operating system. The predictive component, operating research efforts on our team, and focus on chal- within a component in the Vista operating system called lenges, opportunities, and directions with the Superfetch, learns by watching sequences of application streaming of machine intelligence into daily life. launches over time to predict a computer user’s application launches. These predictions, coupled with a utility model 1 Reflections on Trends and Directions that captures preferences about the cost of waiting, are used Over the last decade, technical and infrastructural develop- in an ongoing optimization to prefetch unlaunched applica- ments have come together to create a nurturing environment tions into memory ahead of their manual launching. The for developing and fielding applications of machine learning implicit service seeks to minimize the average wait for ap- and reasoning—and for harnessing automated intelligence -
F. Alex Feltus, Ph.D
F. Alex Feltus, Ph.D. Curriculum Vitae 001010101000001000100001011001010101001000101001010010000100001010101001001000010010001000100001010001001010100100010001000101001000011110101000110010100010101010101010110101010100001000010010101010100100100000101001010010001010110100010 Clemson University • Department of Genetics & Biochemistry Biosystems Research Complex Rm 302C • 105 Collings St. • Clemson, SC 29634 (864)656-3231 (office) • (864) 654-5403 (home) • Skype: alex.feltus • [email protected] https://www.clemson.edu/science/departments/genetics-biochemistry/people/profiles/ffeltus https://orcid.org/0000-0002-2123-6114 https://www.linkedin.com/in/alex-feltus-86a0073a 001010101000001010101010101010101010100110000101100101010100100010100101001000010000101010100100100001001000100010000101000100101010010001000100010100100001111010100011001010001000001000010010101010100100100000101001010010001010110100010 Educational Background: Ph.D. Cell Biology (2000) Vanderbilt University (Nashville, TN) B.Sc. Biochemistry (1992) Auburn University (Auburn, AL) Ph.D. Dissertation Title: Transcriptional Regulation of Human Type II 3β-Hydroxysteroid Dehydrogenase: Stat5- Centered Control by Steroids, Prolactin, EGF, and IL-4 Hormones. Professional Experience: 2018- Professor, Clemson University Department of Genetics and Biochemistry 2017- Core Faculty, Biomedical Data Science and Informatics (BDSI) PhD Program 2018- Faculty Member, Clemson Center for Human Genetics 2020- Faculty Scholar, Clemson University School of Health Research (CUSHR) 2019- co-Founder, -
The Dawn of Artificial Intelligence Hearing
S. HRG. 114–562 THE DAWN OF ARTIFICIAL INTELLIGENCE HEARING BEFORE THE SUBCOMMITTEE ON SPACE, SCIENCE, AND COMPETITIVENESS OF THE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION UNITED STATES SENATE ONE HUNDRED FOURTEENTH CONGRESS SECOND SESSION NOVEMBER 30, 2016 Printed for the use of the Committee on Commerce, Science, and Transportation ( U.S. GOVERNMENT PUBLISHING OFFICE 24–175 PDF WASHINGTON : 2017 For sale by the Superintendent of Documents, U.S. Government Publishing Office Internet: bookstore.gpo.gov Phone: toll free (866) 512–1800; DC area (202) 512–1800 Fax: (202) 512–2104 Mail: Stop IDCC, Washington, DC 20402–0001 VerDate Nov 24 2008 13:07 Feb 15, 2017 Jkt 075679 PO 00000 Frm 00001 Fmt 5011 Sfmt 5011 S:\GPO\DOCS\24175.TXT JACKIE SENATE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION ONE HUNDRED FOURTEENTH CONGRESS SECOND SESSION JOHN THUNE, South Dakota, Chairman ROGER F. WICKER, Mississippi BILL NELSON, Florida, Ranking ROY BLUNT, Missouri MARIA CANTWELL, Washington MARCO RUBIO, Florida CLAIRE MCCASKILL, Missouri KELLY AYOTTE, New Hampshire AMY KLOBUCHAR, Minnesota TED CRUZ, Texas RICHARD BLUMENTHAL, Connecticut DEB FISCHER, Nebraska BRIAN SCHATZ, Hawaii JERRY MORAN, Kansas EDWARD MARKEY, Massachusetts DAN SULLIVAN, Alaska CORY BOOKER, New Jersey RON JOHNSON, Wisconsin TOM UDALL, New Mexico DEAN HELLER, Nevada JOE MANCHIN III, West Virginia CORY GARDNER, Colorado GARY PETERS, Michigan STEVE DAINES, Montana NICK ROSSI, Staff Director ADRIAN ARNAKIS, Deputy Staff Director JASON VAN BEEK, General Counsel KIM LIPSKY, Democratic -
Report on the Future of AI Workshop
Report on The Future of AI Workshop Date: December 13 - 15, 2002 Venue: Amagi Homestead, IBM Japan Edited by the Future of AI Workshop Steering Committee Edward A. Feigenbaum Setsuo Ohsuga Hiroshi Motoda Koji Sasaki Compiled by AdIn Research, Inc. August 31, 2003 Please direct general inquiries to AdIn Research, Inc. 3-6 Kioicho, Chiyoda-ku, Tokyo 102-0094, Japan phone: +81-3-3288-7311 fax: +81-3-3288-7568 url: http://www.adin.co.jp/ Table of Contents Prospectus ………………………………………………………………………………………………… 4 Outline …………………………………………………………………………………………………… 5 Co-sponsors and Supporting Organizations ………………………………………………………………… 6 Schedule ……………………………………………………………………………………………………. 9 List of Panelists …………………………………………………………………………………………….. 10 Contact Information …………………………………….…………………………………………………... 12 Keynote Speech Edward A. Feigenbaum ………………………………………………………………... 17 Sessions 1. FOUNDATIONS OF AI 1. Hiroki Arimura ………………………………………………………………………… 27 2. Stuart Russell …………………………………………………………………………... 30 3. Naonori Ueda …………………………………………………………………………... 34 4. Akito Sakurai …………………………………………………………………………... 38 2. DISCOVERY 1. Einoshin Suzuki ……………………………………………………………………...… 45 2. Satoru Miyano …………………………………………………………………...…….. 49 3. Thomas Dietterich …………………………………………………………...………… 55 4. Hiroshi Motoda ………………………………………………………...……………… 62 3. HCL 1. Yasuyuki Sumi …………………………………………………...……………………. 68 2. Kumiyo Nakakoji ………………………………………………...……………………. 72 3. Toru Ishida ………………………………………………………..…………………… 77 4. Eric Horvitz ………………………………………………………..…………………... 83 4. AI SYSTEMS 1. Ron -
TR10: Modeling Surprise Technologyreview.Com NSORS
NSORS Modeling Surprise WHO Eric Horvitz, Microsoft Research Combining massive quantities of data, insights into human psychology, and machine learning can help humans manage DEFINITION surprising events, says Eric Horvitz. Surprise modeling combines data mining and machine learning to By M. Mitchell Waldrop help people do a better job of anticipating and coping with Much of modern life depends on forecasts: where the next unusual events. hurricane will make landfall, how the stock market will react IMPACT to falling home prices, who will win the next primary. While Although research in the field is existing computer models predict many things fairly preliminary, surprise modeling accurately, surprises still crop up, and we probably can’t could aid decision makers in a wide eliminate them. But Eric Horvitz, head of the Adaptive range of domains, such as traffic management, preventive medicine, Systems and Interaction group at Microsoft Research, thinks military planning, politics, business, we can at least minimize them, using a technique he calls and finance. “surprise modeling.” CONTEXT A prototype that alerts users to Horvitz stresses that surprise modeling is not about building a surprises in Seattle traffic patterns technological crystal ball to predict what the stock market will has proved effective in field tests do tomorrow, or what al-Qaeda might do next month. But, he involving thousands of Microsoft says, “We think we can apply these methodologies to look at employees. Studies investigating broader applications are now under the kinds of things that have surprised us in the past and then way. model the kinds of things that may surprise us in the future.” The result could be enormously useful for decision makers in fields that range from health care to military strategy, politics to financial markets.