PSB 2012 Attendees (As of December 16, 2011)

Total Page:16

File Type:pdf, Size:1020Kb

PSB 2012 Attendees (As of December 16, 2011) PSB 2012 Attendees (as of December 16, 2011) Pankaj Agarwal Ekaterina Buyko GlaxoSmithKline R&D University of Jena Russ Altman William Cannon Stanford University Pacific Northwest National Lab Brenda Andrews Greg Carter University of Toronto The Jackson Laboratory Gretta Armstrong Hannah Carter Penn State University Johns Hopkins University Ferhat Ay Li-Wei Chang University of Washington Washington University Joel Bader Meiping Chang Johns Hopkins University Regeneron Pharmaceuticals Pierre Baldi Li Chen UCI Johns Hopkins University Nirmalya Bandyopadhyay Richard Chen University of Florida, Gainesville Personalis Stephane Bourgeois Xiaoying Chen Wellcome Trust Sanger Institute Roche Molecular Systems, Inc. John Boyle Yian Chen ISB Moffitt Cancer Center Steven Brenner Jianlin Cheng University of California, Berkeley University of Missouri, Columbia Fiona Brinkman Chakra Chennubhotla Simon Fraser University University of Pittsburgh Yana Bromberg Sun Choi Rutgers University Ewha Womans University Carrie Buchanan Brock Christensen Vanderbilt University Medical Center Dartmouth Medical School John Bunge Kevin Bretonnel Cohen Cornell University U. Colorado School of Medicine, Computational Bioscience Program William Bush Vanderbilt University Clare Bates Congdon University of Southern Maine PSB 2012 Attendees (as of December 16, 2011) Ross Curtis Alex Frase Carnegie Mellon University The Pennsylvania State University Denise Daley Brooke Fridley University of British Columbia Mayo Clinic Rishika De Julia Fukuyama Computational Genetics Lab Dartmouth Stanford College Jianjiong Gao Francisco De La Vega Memorial Sloan-Kettering Cancer Stanford University Sara Garamszegi Valentin Dinu Boston University Arizona State University Yael Garten Thomas Doak Stanford University Indiana University Richard Gayle Scott Dudek SpreadingScience Vanderbilt University Georgios Gkoutos A. Keith Dunker University of Cambridge Indiana University Anna Goldenberg Howard Edenberg SickKids Research Institute Indiana Univ School of Medicine Ajay Gopinathan Doug Fenger University of California, Merced Dart Neuroscience Raluca Gordan Krzysztof Fidelis Duke University University of California, Davis Benjamin Grady Charles Fisher Vanderbilt University Harvard University Jiang Gui Aris Floratos Dartmouth Medical School Columbia University/Assistant Professor Xin Guo Roberto Flores Duke University National Cancer Institute Udo Hahn James Foster Friedrich-Schiller-Universität Jena University of Idaho Greg Hampikian Eric Franzosa Boise State University Boston University PSB 2012 Attendees (as of December 16, 2011) Alexander Hartemink Fan Jin Duke University Peking University David Haussler Tamer Kahveci University of California, Santa Cruz University of Florida Jamie Heywood Maricel Kann PatientsLikeMe UMBC Akihiro Hirakawa Rachel Karchin Tokyo University of Science Johns Hopkins University Jim Holloway Konrad Karczewski ZymoGenetics, a BMS company Stanford University Susan Holmes Neerja Katiyar Stanford University Pennsylvania State University Emily Holzinger Sarah Killcoyne Vanderbilt University ISB James Holzwarth Carol Kim Nestec Ltd University Of Maine Wei-Lun Hsu Ju Han Kim Indiana University Seoul National University College of Medicine Jing Hu Franklin & Marshall College Philip Kim University of Toronto Fei Huang IU school of medicine Carl Kingsford University of Maryland Tim Hughes University of Toronto Hiroaki Kitano Okinawa Institute of Science and Lawrence Hunter Technology University of Colorado School of Medicine Teri Klein Daniel Hyduke Stanford University University of California, San Diego Judith Klein-Seetharaman Trey Ideker University of Pittsburgh University of California San Diego Devin Koestler Chan-Seok Jeong Dartmouth Medical School Korea Advanced Institute of Science and Technology (KAIST) PSB 2012 Attendees (as of December 16, 2011) Richard Kriwacki Jason McDermott St. Jude Children’s Research Hospital Pacific Northwest National Laboratory Yinglei Lai Paul Joseph McMurdie The George Washington University Stanford University Brad Langhorst Eric Meslin New England Biolabs Indiana University Koby Levy Tijana Milenkovic Weizmann Institute of Science University of Notre Dame Robert Li Siavash Mir Arabbaygi USDA University of Texas at Austin Bryan Linggi Leonid Mirny PNNL MIT Jennifer Listgarten Shahin Mohammadi Microsoft Research Purdue University Tianyun Liu Sean Mooney Stanford University/Research Associate Buck Institute for Research on Aging Rochelle Long BJ Morrison McKay NIGMS, NIH ISCB Xinghua Lu David Mrazek University of Pittsburgh Mayo Clinic Yi Mao Susan Mulfinger University of Washington Penn State University Costas Maranas Kevin Mwenda Penn State Dartmouth Medical School Elaine Mardis Nam-Phuong Nguyen Washington University School of Medicine University of Texas at Austin Andre Marette William Noble Laval University The University of Washington Scott Markel Michael Ochs ISCB / Accelrys Johns Hopkins University Gabor Marth Anika Oellrich Boston College European Bioinformatics Institute PSB 2012 Attendees (as of December 16, 2011) Octavio Pajaro Steve Scherer Mayo Clinic Baylor College of Medicine Ranadip Pal Joe Schoeniger Texas Tech University Sandia National Labs Chan Hee Park Zack Scholl Seoul National University College of Duke University Medicine Junhee Seok Jyotishman Pathak Stanford University Mayo Clinic Shefali Setia Matteo Pellegrini The Pennsylvania State University UCLA James Sikela Sarah Pendergrass University of Colorado Denver The Center for Systems Genomics, The Pennsylvania State University Amit Sinha Dana-Farber Cancer Institute Zhenling Peng University of Alberta Owen Solberg Sandia National Labs Bethany Percha Stanford University Victor Solovyev RHUL, University of London Todd Peterson Life Technologies Corporation Paul Spellman Oregon Health & Science University Teresa Przytycka NIH Daron Standley WPI Immunology Frontier Research Center Maria Ritchie (IFReC) Vanderbilt University Joshua Stuart Marylyn Ritchie University of California, Santa Cruz The Pennsylvania State University Collin Stultz David Rocke Massachusetts Institute of Technology Univ. of California, Davis Arvis Sulovari Tal Ronnen Oron Dartmouth College Buck Institute for Research on Aging Yanni Sun V. Ram Samudrala Michigan State University University of Washington Nicholas Tatonetti Stanford University PSB 2012 Attendees (as of December 16, 2011) Jeff Thompson Mark Woon University of Southern Maine Stanford University Makoto Tomita Lei Xie Tokyo Medical and Dental University Hunter College, CUNY Orly Ullman Hongyi Xin MIT Carnegie Mellon University Fabio Vandin Dong Xu Brown University University of Missouri Anurag Verma Hua Xu The Pennsylvania State University Vanderbilt University Thanh Vu Junming Yin Stanford Univ. & PAVAHCS Carnegie Mellon University Randy Wadkins Bing Zhang University of Mississippi Vanderbilt University Tandy Warnow Kam Zhang University of Texas at Austin RIKEN Rene Warren Quan Zhang Michael Smith Genome Sciences Centre Indiana University Bloomington Katrina Waters Yuan Zhang Pacific Northwest National Lab Michigan State University Ryan Whaley Wenjin Zheng Stanford University Medical University of South Carolina Michelle Whirl-Carrillo Jianling Zhong Stanford University Duke University Eric Wieben Mayo Clinic Kelly Williams Sandia National Laboratories David Wishart University of Alberta Damian Wojtowicz National Institutes of Health .
Recommended publications
  • Algorithms for Computational Biology 8Th International Conference, Alcob 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings
    Lecture Notes in Bioinformatics 12715 Subseries of Lecture Notes in Computer Science Series Editors Sorin Istrail Brown University, Providence, RI, USA Pavel Pevzner University of California, San Diego, CA, USA Michael Waterman University of Southern California, Los Angeles, CA, USA Editorial Board Members Søren Brunak Technical University of Denmark, Kongens Lyngby, Denmark Mikhail S. Gelfand IITP, Research and Training Center on Bioinformatics, Moscow, Russia Thomas Lengauer Max Planck Institute for Informatics, Saarbrücken, Germany Satoru Miyano University of Tokyo, Tokyo, Japan Eugene Myers Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Marie-France Sagot Université Lyon 1, Villeurbanne, France David Sankoff University of Ottawa, Ottawa, Canada Ron Shamir Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Terry Speed Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia Martin Vingron Max Planck Institute for Molecular Genetics, Berlin, Germany W. Eric Wong University of Texas at Dallas, Richardson, TX, USA More information about this subseries at http://www.springer.com/series/5381 Carlos Martín-Vide • Miguel A. Vega-Rodríguez • Travis Wheeler (Eds.) Algorithms for Computational Biology 8th International Conference, AlCoB 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings 123 Editors Carlos Martín-Vide Miguel A. Vega-Rodríguez Rovira i Virgili University University of Extremadura Tarragona, Spain Cáceres, Spain Travis Wheeler University of Montana Missoula, MT, USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Bioinformatics ISBN 978-3-030-74431-1 ISBN 978-3-030-74432-8 (eBook) https://doi.org/10.1007/978-3-030-74432-8 LNCS Sublibrary: SL8 – Bioinformatics © Springer Nature Switzerland AG 2021 This work is subject to copyright.
    [Show full text]
  • The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-Rnns and the Protein Structure Prediction Problem
    Journal of Machine Learning Research 4 (2003) 575-602 Submitted 2/02; Revised 4/03; Published 9/03 The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-RNNs and the Protein Structure Prediction Problem Pierre Baldi [email protected] Gianluca Pollastri [email protected] School of Information and Computer Science Institute for Genomics and Bioinformatics University of California, Irvine Irvine, CA 92697-3425, USA Editor: Michael I. Jordan Abstract We describe a general methodology for the design of large-scale recursive neural network architec- tures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connec- tions to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dy- namics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed.
    [Show full text]
  • Methodology for Predicting Semantic Annotations of Protein Sequences by Feature Extraction Derived of Statistical Contact Potentials and Continuous Wavelet Transform
    Universidad Nacional de Colombia Sede Manizales Master’s Thesis Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform Author: Supervisor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez A thesis submitted in fulfillment of the requirements for the degree of Master’s on Engineering - Industrial Automation in the Department of Electronic, Electric Engineering and Computation Signal Processing and Recognition Group June 2014 Universidad Nacional de Colombia Sede Manizales Tesis de Maestr´ıa Metodolog´ıapara predecir la anotaci´on sem´antica de prote´ınaspor medio de extracci´on de caracter´ısticas derivadas de potenciales de contacto y transformada wavelet continua Autor: Tutor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez Tesis presentada en cumplimiento a los requerimientos necesarios para obtener el grado de Maestr´ıaen Ingenier´ıaen Automatizaci´onIndustrial en el Departamento de Ingenier´ıaEl´ectrica,Electr´onicay Computaci´on Grupo de Procesamiento Digital de Senales Enero 2014 UNIVERSIDAD NACIONAL DE COLOMBIA Abstract Faculty of Engineering and Architecture Department of Electronic, Electric Engineering and Computation Master’s on Engineering - Industrial Automation Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform by Gustavo Alonso Arango Argoty In this thesis, a method to predict semantic annotations of the proteins from its primary structure is proposed. The main contribution of this thesis lies in the implementation of a novel protein feature representation, which makes use of the pairwise statistical contact potentials describing the protein interactions and geometry at the atomic level.
    [Show full text]
  • Deep Learning in Chemoinformatics Using Tensor Flow
    UC Irvine UC Irvine Electronic Theses and Dissertations Title Deep Learning in Chemoinformatics using Tensor Flow Permalink https://escholarship.org/uc/item/963505w5 Author Jain, Akshay Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Deep Learning in Chemoinformatics using Tensor Flow THESIS submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in Computer Science by Akshay Jain Thesis Committee: Professor Pierre Baldi, Chair Professor Cristina Videira Lopes Professor Eric Mjolsness 2017 c 2017 Akshay Jain DEDICATION To my family and friends. ii TABLE OF CONTENTS Page LIST OF FIGURES v LIST OF TABLES vi ACKNOWLEDGMENTS vii ABSTRACT OF THE THESIS viii 1 Introduction 1 1.1 QSAR Prediction Methods . .2 1.2 Deep Learning . .4 2 Artificial Neural Networks(ANN) 5 2.1 Artificial Neuron . .5 2.2 Activation Function . .7 2.3 Loss function . .8 2.4 Optimization . .8 3 Deep Recursive Architectures 10 3.1 Recurrent Neural Networks (RNN) . 10 3.2 Recursive Neural Networks . 11 3.3 Directed Acyclic Graph Recursive Neural Networks (DAG-RNN) . 11 4 UG-RNN for small molecules 14 4.1 DAG Generation . 16 4.2 Local Information Vector . 16 4.3 Contextual Vectors . 17 4.4 Activity Prediction . 17 4.5 UG-RNN With Contracted Rings (UG-RNN-CR) . 18 4.6 Example: UG-RNN Model of Propionic Acid . 20 5 Implementation 24 6 Data & Results 26 6.1 Aqueous Solubility Prediction . 26 6.2 Melting Point Prediction . 28 iii 7 Conclusions 30 Bibliography 32 A Source Code 37 A.1 UGRNN .
    [Show full text]
  • Are Profile Hidden Markov Models Identifiable?
    Are Profile Hidden Markov Models Identifiable? Srilakshmi Pattabiraman Tandy Warnow Department of Electrical and Computer Engineering Department of Computer Science University of Illinois at Urbana-Champaign University of Illinois at Urbana-Champaign Urbana, Illinois Urbana, Illinois [email protected] [email protected] ABSTRACT 1 INTRODUCTION Profile Hidden Markov Models (HMMs) are graphical models that Profile Hidden Markov Models (HMMs) are arguably themost can be used to produce finite length sequences from a distribution. common statistical models in bioinformatics. Originally introduced In fact, although they were only introduced for bioinformatics 25 by Haussler and colleagues in [10, 12], and then expanded later years ago (by Haussler et al., Hawaii International Conference on in many subsequent texts [4–6, 9, 11, 21, 25], profile HMMs are Systems Science 1993), they are arguably the most commonly used now used in many analytical steps in biological sequence analysis statistical model in bioinformatics, with multiple applications, in- [15, 17–19, 22]. cluding protein structure and function prediction, classifications Profile Hidden Markov models are graphical models with match of novel proteins into existing protein families and superfamilies, states, insertion states, and deletion states; and the match and in- metagenomics, and multiple sequence alignment. The standard use sertion states emit letters from an underlying alphabet Σ (i.e., Σ of profile HMMs in bioinformatics has two steps: first a profile may be the 20 amino acids, the four nucleotides, or some other HMM is built for a collection of molecular sequences (which may set of symbols). In the standard form presented in [4] (widely in not be in a multiple sequence alignment), and then the profile HMM use in bioinformatics applications), each profile Hidden Markov is used in some subsequent analysis of new molecular sequences.
    [Show full text]
  • Computational Biology and Bioinformatics
    Vol. 30 ISMB 2014, pages i1–i2 BIOINFORMATICS EDITORIAL doi:10.1093/bioinformatics/btu304 Editorial This special issue of Bioinformatics serves as the proceedings of The conference used a two-tier review system, a continuation the 22nd annual meeting of Intelligent Systems for Molecular and refinement of a process begun with ISMB 2013 in an effort Biology (ISMB), which took place in Boston, MA, July 11–15, to better ensure thorough and fair reviewing. Under the revised 2014 (http://www.iscb.org/ismbeccb2014). The official confer- process, each of the 191 submissions was first reviewed by at least ence of the International Society for Computational Biology three expert referees, with a subset receiving between four and (http://www.iscb.org/), ISMB, was accompanied by 12 Special eight reviews, as needed. These formal reviews were frequently Interest Group meetings of one or two days each, two satellite supplemented by online discussion among reviewers and Area meetings, a High School Teachers Workshop and two half-day Chairs to resolve points of dispute and reach a consensus on tutorials. Since its inception, ISMB has grown to be the largest each paper. Among the 191 submissions, 29 were conditionally international conference in computational biology and bioinfor- accepted for publication directly from the first round review Downloaded from matics. It is expected to be the premiere forum in the field for based on an assessment of the reviewers that the paper was presenting new research results, disseminating methods and tech- clearly above par for the conference. A subset of 16 papers niques and facilitating discussions among leading researchers, were viewed as potentially in the top tier but raised significant practitioners and students in the field.
    [Show full text]
  • Course Outline
    Department of Computer Science and Software Engineering COMP 6811 Bioinformatics Algorithms (Reading Course) Fall 2019 Section AA Instructor: Gregory Butler Curriculum Description COMP 6811 Bioinformatics Algorithms (4 credits) The principal objectives of the course are to cover the major algorithms used in bioinformatics; sequence alignment, multiple sequence alignment, phylogeny; classi- fying patterns in sequences; secondary structure prediction; 3D structure prediction; analysis of gene expression data. This includes dynamic programming, machine learning, simulated annealing, and clustering algorithms. Algorithmic principles will be emphasized. A project is required. Outline of Topics The course will focus on algorithms for protein sequence analysis. It will not cover genome assembly, genome mapping, or gene recognition. • Background in Biology and Genomics • Sequence Alignment: Pairwise and Multiple • Representation of Protein Amino Acid Composition • Profile Hidden Markov Models • Specificity Determining Sites • Curation, Annotation, and Ontologies • Machine Learning: Secondary Structure, Signals, Subcellular Location • Protein Families, Phylogenomics, and Orthologous Groups • Profile-Based Alignments • Algorithms Based on k-mers 1 Texts | in Library D. Higgins and W. Taylor (editors). Bioinformatics: Sequence, Structure and Databanks, Oxford University Press, 2000. A. D. Baxevanis and B. F. F. Ouelette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Wiley, 1998. Richard Durbin, Sean R. Eddy, Anders Krogh,
    [Show full text]
  • Deep Learning in Science Pierre Baldi Frontmatter More Information
    Cambridge University Press 978-1-108-84535-9 — Deep Learning in Science Pierre Baldi Frontmatter More Information Deep Learning in Science This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuro- science. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking. Pierre Baldi is Distinguished Professor of Computer Science at University of California, Irvine. His main research interest is understanding intelligence in brains and machines. He has made seminal contributions to the theory of deep learning and its applications to the natural sciences, and has written four other books. © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-1-108-84535-9 — Deep Learning in
    [Show full text]
  • Bringing Folding Pathways Into Strand Pairing Prediction
    Bringing folding pathways into strand pairing prediction Jieun Jeong1,2, Piotr Berman1, and Teresa Przytycka2 1 Computer Science and Engineering Department The Pennsylvania State University University Park, PA 16802 USA 2 National Center for Biotechnology Information US National Library of Medicine, National Institutes of Health Bethesda, MD 20894 email: [email protected], [email protected], [email protected] Abstract. The topology of β-sheets is defined by the pattern of hydrogen- bonded strand pairing. Therefore, predicting hydrogen bonded strand partners is a fundamental step towards predicting β-sheet topology. In this work we report a new strand pairing algorithm. Our algorithm at- tempts to mimic elements of the folding process. Namely, in addition to ensuring that the predicted hydrogen bonded strand pairs satisfy basic global consistency constraints, it takes into account hypothetical folding pathways. Consistently with this view, introducing hydrogen bonds be- tween a pair of strands changes the probabilities of forming other strand pairs. We demonstrate that this approach provides an improvement over previously proposed algorithms. 1 Introduction The prediction of protein structure from protein sequence is a long-held goal that would provide invaluable information regarding the function of individ- ual proteins and the evolution of protein families. The increasing amount of sequence and structure data, made it possible to decouple the structure predic- tion problem from the problem of modeling of protein folding process. Indeed, a significant progress has been achieved by bioinformatics approaches such as homology modeling, threading, and assembly from fragments [16]. At the same time, the fundamental problem of how actually a protein acquires its final folded state remains a subject of controversy.
    [Show full text]
  • Research News
    Computing Research News COMPUTING RESEARCH ASSOCIATION, CELEBRATING 40 YEARS OF SERVICE TO THE COMPUTING RESEARCH COMMUNITY JUNE 2013 Vol. 25 / No. 6 Announcements 2 Coalition for National Science Funding 2 CRA Announces Outstanding Undergraduate Researcher Award Winners 3 Computing Research in Action 5 CERP Infographic 6 NSF Funding Opportunity 6 CRA Recognizes Participants 7 CRA Board Members 16 CRA Board Officers 16 CRA Staff 16 Professional Opportunities 17 COMPUTING RESEARCH NEWS, JUNE 2013 Vol. 25 / No. 6 Announcements 2012 Taulbee Report Updated May 15, 2013 Corrected Table F6 Click here to download updated version CRA Releases Latest Research Issue Report New Technology-based Models for Postsecondary Learning: Conceptual Frameworks and Research Agendas The report details the findings of a National Science Foundation-Sponsored Computing Research Association Workshop held at MIT on January 9-11, 2013. From the report: “Advances in technology and in knowledge about expertise, learning, and assessment have the potential to reshape the many forms of education and training past matriculation from high school. In the next decade, higher education, military and workplace training, and professional development must all transform to exploit the opportunities of a new era, leveraging emerging technology-based models that can make learning more efficient and possibly improve student support, all at lower cost for a broader range of learners.” The report is now available as a pdf at http://cra.org/resources/research-issues/. Slides from the presentation at NSF on April 19, 2013 are also available. Investments in STEM Research and Education: Fueling American Innovation On May 7, at the Rayburn House Office Building in Brett Bode from the National Center for Supercomputing Washington, DC, the Coalition for National Science Funding Applications at University of Illinois Urbana-Champaign were (CNSF) held its 19th annual exhibition and reception, on hand to talk about the “Blue Waters” project.
    [Show full text]
  • ACDL-2020-Programme-Ver10.0
    3rd Advanced Course on Data Science &Machine Learning – ACDL 2020 Certosa di Pontignano - Siena, Tuscany, Italy 13 – 17 July 2020 Schedule Ver. 11.0 – July 9th Time Zone: Central European Summer Time (CEST), Offset: UTC+2, Rome – (GMT+2:00) Rome Mon, 13 July Tue, 14 July Wed, 15 July Thu, 16 July Fri, 17 July 07:30 – 09:00 Breakfast Breakfast Breakfast Breakfast Breakfast 09:00 – 09:50 T. Viehmann Michael 8:30 Social Tour D. Bacciu D. Bacciu Tutorial Bronstein Tutorial Tutorial 09:50 – 10:40 Michael Igor Bronstein Babuschkin Tutorial Guided Visit 10:40 – 11:20 Coffee break Coffee break of Siena Coffee break Coffee break 11:20 – 12:10 T. Viehmann Michael Igor Igor Tutorial Bronstein Babuschkin Babuschkin Tutorial 12:10 – 13:00 Michael G. Fiameni Diederik P. Bronstein Industrial Talk Kingma Tutorial 13:00 – 15:00 Lunch (13:00-14:00) Lunch Lunch Lunch Lunch 15:00 – 15:50 Mihaela van José C. Lorenzo De Mattei Guido Sergiy Butenko der Schaar Principe Industrial Talk Sanguinetti 15:50 – 16:40 (14:00-16:40) José C. Guido Guido Sergiy Butenko Principe Sanguinetti Sanguinetti 16:40 – 17:20 Coffee break Coffee break Coffee break Coffee break Coffee break 17:20 – 18:10 17:20 Guided José C. Pierre Baldi Risto Marco Gori Visit of the Principe Miikkulainen 18:10 – 19:00 Certosa di Roman Pierre Baldi Risto Marco Gori Pontignano Belavkin Miikkulainen 19:00 – 19:50 & Roman Pierre Baldi Risto Varun Ojha 18:20 Wine Belavkin Miikkulainen Tutorial Tasting 19:50 – 21:50 Dinner Dinner Dinner Dinner Social Dinner 21:50 – Oral Presentation Roman Oral Presentation Session Belavkin Session with Cantucci with Cantucci with Cantucci biscuits and Vin biscuits and Vin biscuits and Vin Santo (sweet wine) Santo Santo Arrival: July 12 (Dinner at 20:30) Departure: July 18 (Breakfast 07:30-09:00) REGISTRATION The registration desk will be located close to the Main Conference Room.
    [Show full text]
  • I S C B N E W S L E T T
    ISCB NEWSLETTER FOCUS ISSUE {contents} President’s Letter 2 Member Involvement Encouraged Register for ISMB 2002 3 Registration and Tutorial Update Host ISMB 2004 or 2005 3 David Baker 4 2002 Overton Prize Recipient Overton Endowment 4 ISMB 2002 Committees 4 ISMB 2002 Opportunities 5 Sponsor and Exhibitor Benefits Best Paper Award by SGI 5 ISMB 2002 SIGs 6 New Program for 2002 ISMB Goes Down Under 7 Planning Underway for 2003 Hot Jobs! Top Companies! 8 ISMB 2002 Job Fair ISCB Board Nominations 8 Bioinformatics Pioneers 9 ISMB 2002 Keynote Speakers Invited Editorial 10 Anna Tramontano: Bioinformatics in Europe Software Recommendations11 ISCB Software Statement volume 5. issue 2. summer 2002 Community Development 12 ISCB’s Regional Affiliates Program ISCB Staff Introduction 12 Fellowship Recipients 13 Awardees at RECOMB 2002 Events and Opportunities 14 Bioinformatics events world wide INTERNATIONAL SOCIETY FOR COMPUTATIONAL BIOLOGY A NOTE FROM ISCB PRESIDENT This newsletter is packed with information on development and dissemination of bioinfor- the ISMB2002 conference. With over 200 matics. Issues arise from recommendations paper submissions and over 500 poster submis- made by the Society’s committees, Board of sions, the conference promises to be a scientific Directors, and membership at large. Important feast. On behalf of the ISCB’s Directors, staff, issues are defined as motions and are discussed EXECUTIVE COMMITTEE and membership, I would like to thank the by the Board of Directors on a bi-monthly Philip E. Bourne, Ph.D., President organizing committee, local organizing com- teleconference. Motions that pass are enacted Michael Gribskov, Ph.D., mittee, and program committee for their hard by the Executive Committee which also serves Vice President work preparing for the conference.
    [Show full text]