PSB 2009 Attendees (As of January 9, 2009) Mario Albrecht Max Planck

Total Page:16

File Type:pdf, Size:1020Kb

PSB 2009 Attendees (As of January 9, 2009) Mario Albrecht Max Planck PSB 2009 Attendees (as of January 9, 2009) Mario Albrecht Steven Brenner Max Planck Institute for Informatics University of California, Berkeley Hesham Ali Andrzej Brodzik University of Nebraska at Omaha The MITRE Corporation Gil Alterovitz Lukas Burger Harvard/MIT Institute of Bioinformatics, University of Basel Gregory Arnold Amgen Gregory Burrows OHSU Adam Asare UCSF/ITN William Bush Vanderbilt University Pierre Baldi University of California Andrea Califano Columbia University Lars Barquist UC Berkeley J. Gregory Caporaso Univeristy of Colorado Denver James Bassingthwaighte Univeristy of Washington Vincent Carey Harvard Medical School Tanya Berger-Wolf University of Illinois at Chicago Yuhui Cheng University of California, San Diego Ghislain Bidaut INSERM U891 Institut Paoli-Calmette Raymond Cheong Johns Hopkins University Marco Blanchette Stowers Institute for Medical Research Annie Chiang Stanford University Ben Blencowe Gilsoo Cho Anthony Bonner Yonsei University University of Toronto Sung Bum Cho Ingrid Borecki Seoul National University Bioinformatics Washington University Kevin Cohen John Bouck University of Colorado Denver Ceres, Inc. Dilek Colak Philip Bourne King Faisal Specialist Hospital and University of California, San Diego Research Centre PSB 2009 Attendees (as of January 9, 2009) James Costello Laura Elnitski Indiana University NHGRI Anneleen Daemen Drew Endy Dept. Electrical Engineering, KULeuven Stanford University Denise Daley Eric Fahrenthold University of British Columbia Yiping Fan Paul Davis St Jude Children's Research Hospital New England Biolabs Aris Floratos Ramana Davuluri Executive Research Director The Wistar Institute Jessica Fong David De Roure National Institutes of Health University of Southampton Andrew Fox Scott Delp Tufts University Stanford University Don Frohlich Valentin Dinu University of St. Thomas Biomedical Informatics, Arizona State University Hironori Fujisawa The Institute of Statistical Mathematics Sorin Draghici Wayne State University Andre Fujita University of Tokyo Chunguang Du Montclair State University Terry Gaasterland SIO-UCSD and ISCB Joel Dudley Stanford University Jean-François Ganghoffer LEMTA - ENSEM Keith Dunker Indiana University Robert Gentleman FHCRC Howard Edenberg IUSM Todd Gibson University of Colorado Jeremy Edwards Carole Goble Jonathan Eisen University of Manchester UC Davis Genome Center Graciela Gonzalez PSB 2009 Attendees (as of January 9, 2009) Jiang Gui Igor Jouline Dartmouth Medical School Oak Ridge National Laboratory Jörg Hakenberg Crystal Kahn Arizona State University Brown University Greg Hampikian Maricel Kann Boise State University University of Maryland, Baltimore County Mira Han Mihailo Kaplarevic IU Informatics University of Delaware Reece Hart Peter Kasson Genentech, Inc. Stanford University Jean Hausser Tony Keaveny Biozentrum, Universität Basel University of California Ian Holmes Roy Kerckhoffs University of California, Berkeley UCSD James Holzwarth Yang Seok Kim Nestle Research Center KyungHee University Xin Hong Oliver King Indiana University BBRI Tim Hughes Carl Kingsford University of Toronto University of Maryland, College Park Lawrence Hunter Judith Klein U Colorado Denver University of Pittsburgh Trey Ideker Teri Klein UCSD Stanford University Steven F. Jennings Sek Won Kong University of Arkansas at Little Rock Children's Hospital Boston Thomas S. Jensen William Krivan Center for Biological Sequence Analysis Protein Advances Inc. Fredrik Johansson Oleksii Kuchaiev Kyushu University University of California, Irvine PSB 2009 Attendees (as of January 9, 2009) Sudhir Kumar Leonard McMillan Biodesign Institute @ ASU University of North Carolina Anne-Francoise Lamblin Rondo Middleton Nestle Purina Research Inhan Lee University of Michigan Antonina Mitrofanova New York University Christina Leslie Memorial Sloan-Kettering Cancer Center Satoru Miyano University of Tokyo Eric Letouzé INSERM U726 Sean Mooney IU School of Medicine Wei Li Baylor College of Medicine Jason Moore Dartmouth College Wei Keat Lim Columbia University Bernard Moret EPFL Howard Lipshitz University of Toronto Hiroshi Mori Tokyo Institute of Technology Chun-Chi Liu University of Southern California Quaid Morris University of Toronto Yves Lussier University of Chicago Kiran Mukhyala Genentech Jacek Majewski McGill University Hiroyuki Nakagawa Dainippon Sumitomo Pharma Co., Ltd. Hiroki Makiguchi Mitsui Knowledge Industry Co., Ltd. Kazuyuki Nakamura The Institute of Statistical Mathematics Duane Malcolm University of Auckland Ozkan Nalbantoglu EE-UNL Costas Maranas Penn State Dok U Nam Gabor Marth Maxwell Neal Boston College Biology Univ. of Washington Jason McDermott Cameron Neylon Pacific Northwest National Laboratory STFC PSB 2009 Attendees (as of January 9, 2009) Kozo Nishida Teresa Przytycka Nara Institute of Science and Technology NCBI /NLM / NIH Michael Ochs Yan Qi Johns Hopkins University Johns Hopkins University Yanay Ofran John Quackenbush Dana-Farber Cancer Institute Christopher Oldfield Center for Computational Biology and Chang Quo Bioinformatic Georgia Tech Eirikur Palsson Amina Qutub Simon Fraser University Johns Hopkins University Feng Pan Predrag Radivojac UNC - Chapel Hill Indiana University Qun Pan Bharath Ramachandran University of Toronto Amgen Inc. Yue Pan Benjamin Raphael UW-Madison Jason Raymond Kristine Pattin University of California, Merced Dartmouth College Martin Reese Matteo Pellegrini Omicia Inc. UCLA Jeffrey Reinbolt John Phan Stanford University Georgia Institute of Technology Habtom Ressom Heather Piwowar Georgetown University University of Pittsburgh Vicente Reyes Corrado Priami Dept. of Biological Sciences CoSBi Marc Riedel Michael Province University of Minnesota Washington University Anna Ritz Robert Pruitt Brown University Purdue University Simon Rosenfeld National Cancer Institute PSB 2009 Attendees (as of January 9, 2009) Sushmita Roy Chantel Sloan Computer Science Dartmouth College David Russell Neil Smalheiser University of Nebraska – Lincoln Univ. of Illinois at Chicago Karen Sachs Victor Solovyev Stanford University School of Medicine London University Frank Sachse Jeroen Stinstra CVRTI, University of Utah SCI Institute Cenk Sahinalp Joshua Stuart Simon Fraser University UCSC Dennis Salahub Yoshinori Tamada University of Calgary Institute of Medical Science, University of Tokyo Nagiza Samatova North Carolina State Univ./Oak Ridge Koichiro Tamura National Lab Tokyo Metropolitan University Ralph Schlapbach Jijun Tang Functional Genomics Center University of South Carolina Matthew Schmidt Ling Fung Tang North Carolina State University University of Hong Kong Daniel Schrider Luis Tari Indiana University Arizona State University Ina Sen Oznur Tastan Arizona State University Carnegie Mellon University Nigam Shah Allison Tegge Stanford University MU Informatics Institute Shai Shen-Orr Jesper Tegnér Stanford University Karolinska Institutet James Sikela Kaitlin Thaney Univ. of Colo. Denver Science Commons Mona Singh Paul Thomas Princeton University SRI International PSB 2009 Attendees (as of January 9, 2009) Glenys Thomson Katrina Waters University of California at Berkeley Pacific Northwest National Lab Olga Troyanskaya Bobbie-Jo Webb-Robertson Princeton University Pacific Northwest National Laboratory Anna Tyler Oscar Westesson Dartmouth College UC Berkeley Ilya Vakser Trish Whetzel University of Kansas Stanford Biomedical Informatics Research Charles Vaske David Wishart UCSC University of Alberta Vincent Voelz Mike Wong Stanford University San Francisco State University Stefano Volinia Shirley Wu Dip. di Morfologia ed Embriologia Stanford University Randy Wadkins Zhong-Ru Xie University of Mississippi Atsuko Yamaguchi (May) Dongmei Wang Database Center for Life Science Georgia Tech and Emory Univ. Jiqin Yang Dennis Wang EMD Serono MRC Biostatistics Unit Kenny Ye Junwen Wang University of Hong Kong Hong Yu University of Wisconsin-Milwaukee Kai Wang Merck & Co., Inc Yu Zhang Pennsylvania State University Wei Wang University of North Carolina Shanrong Zhao Johnson & Johnson Yang Wang Agencourt Bioscience Wei Zhu MedImmune Inc. Tandy Warnow The University of Texas at Austin .
Recommended publications
  • RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY
    RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press ISSN: 1790-5125 www.wseas.org ISBN: 978-960-474-141-0 RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press www.wseas.org Copyright © 2009, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed
    [Show full text]
  • BIOINFORMATICS Doi:10.1093/Bioinformatics/Bti144
    Vol. 00 no. 0 2004, pages 1–11 BIOINFORMATICS doi:10.1093/bioinformatics/bti144 Solving and analyzing side-chain positioning problems using linear and integer programming Carleton L. Kingsford, Bernard Chazelle and Mona Singh∗ Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, 35, Olden Street, Princeton, NJ 08544, USA Received on August 1, 2004; revised on October 10, 2004; accepted on November 8, 2004 Advance Access publication … ABSTRACT set of possible rotamer choices (Ponder and Richards, 1987; Motivation: Side-chain positioning is a central component of Dunbrack and Karplus, 1993) for each Cα position on the homology modeling and protein design. In a common for- backbone. The goal is to choose a rotamer for each position mulation of the problem, the backbone is fixed, side-chain so that the total energy of the molecule is minimized. This conformations come from a rotamer library, and a pairwise formulation of SCP has been the basis of some of the more energy function is optimized. It is NP-complete to find even a successful methods for homology modeling (e.g. Petrey et al., reasonable approximate solution to this problem. We seek to 2003; Xiang and Honig, 2001; Jones and Kleywegt, 1999; put this hardness result into practical context. Bower et al., 1997) and protein design (e.g. Dahiyat and Mayo, Results: We present an integer linear programming (ILP) 1997; Malakauskas and Mayo, 1998; Looger et al., 2003). In formulation of side-chain positioning that allows us to tackle homology modeling, the goal is to predict the structure for a large problem sizes.
    [Show full text]
  • 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]
  • • La Gestion Efficace De L’Énergie • Le Réseautage Planétaire • Les Processus Géophysiques • L’Économie Globale, La Sécurité Et La Stabilité
    SupérieureS atiqueS athéM e M inaire D SéM The planet on which we live and the challenges that we face on this planet become increasingly complex as ecological, economic and social systems are large intertwined networks governed by dynamic processes and feedback loops. Mathematical models are indispensable in understanding and managing such systems since they provide insight into governing processes; they help predict future behavior; and they allow for risk-free evaluation of possible interventions. The goal of this thematic program is to tackle pressing and emerging challenges in population and ecosystem health, including understanding and controlling major transmissible diseases, optimizing and monitoring vaccination, predicting the impacts of climate change on invasive species, protecting biodiversity and managing ecosystems sustainably. This pan-Canadian program will bring together the international community of researchers who work on these topics in a series of workshops to foster exchange and stimulate cross-disciplinary research between all scientific areas involved, to discuss perspectives and directions for future advances in the field, including new models and methods and to foster tighter links between the research community, government agencies and policy makers. Three summer schools will introduce graduate students and postdoctoral fellows to the art of modeling living systems and to the latest tools and techniques to analyze these models. SCIENTIFIC COMMITTEE Jacques Bélair Mark Lewis (Montréal) Models and Methods in Ecology (Alberta) Frithjof Lutscher and Epidemiology Mathematical Modeling James Watmough (Ottawa) February 6-8, 2013 (UNB) of Indigenous Population Jianhong Wu CRM, Montréal (York) Organizers: Jacques Bélair (Montréal), Health Jianhong Wu (York) September 28-29, 2013 AISENSTADT CHAIRS BIRS Bryan Grenfell (Princeton), May 2013 Graphic Design: www.neograf.ca Simon A.
    [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]
  • BIOGRAPHICAL SKETCH NAME: Berger
    BIOGRAPHICAL SKETCH NAME: Berger, Bonnie eRA COMMONS USER NAME (credential, e.g., agency login): BABERGER POSITION TITLE: Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.) EDUCATION/TRAINING DEGREE Completion (if Date FIELD OF STUDY INSTITUTION AND LOCATION applicable) MM/YYYY Brandeis University, Waltham, MA AB 06/1983 Computer Science Massachusetts Institute of Technology SM 01/1986 Computer Science Massachusetts Institute of Technology Ph.D. 06/1990 Computer Science Massachusetts Institute of Technology Postdoc 06/1992 Applied Mathematics A. Personal Statement Advances in modern biology revolve around automated data collection and sharing of the large resulting datasets. I am considered a pioneer in the area of bringing computer algorithms to the study of biological data, and a founder in this community that I have witnessed grow so profoundly over the last 26 years. I have made major contributions to many areas of computational biology and biomedicine, largely, though not exclusively through algorithmic innovations, as demonstrated by nearly twenty thousand citations to my scientific papers and widely-used software. In recognition of my success, I have just been elected to the National Academy of Sciences and in 2019 received the ISCB Senior Scientist Award, the pinnacle award in computational biology. My research group works on diverse challenges, including Computational Genomics, High-throughput Technology Analysis and Design, Biological Networks, Structural Bioinformatics, Population Genetics and Biomedical Privacy. I spearheaded research on analyzing large and complex biological data sets through topological and machine learning approaches; e.g.
    [Show full text]
  • Fall 2016 Is Available in the Laboratory of Dr
    RNA Society Newsletter Aug 2016 From the Desk of the President, Sarah Woodson Greetings to all! I always enjoy attending the annual meetings of the RNA Society, but this year’s meeting in Kyoto was a standout in my opinion. This marked the second time that the RNA meeting has been held in Kyoto as a joint meeting with the RNA Society of Japan. (The first time was in 2011). Particular thanks go to the local organizers Mikiko Siomi and Tom Suzuki who took care of many logistical details, and to all of the organizers, Mikiko, Tom, Utz Fischer, Wendy Gilbert, David Lilley and Erik Sontheimer, for putting together a truly exciting and stimulating scientific program. Of course, the real excitement in the annual RNA meetings comes from all of you who give the talks and present the posters. I always enjoy meeting old friends and colleagues, but the many new participants in this year’s meeting particularly encouraged me. (Continued on p2) In this issue : Desk of the President, Sarah Woodson 1 Highlights of RNA 2016 : Kyoto Japan 4 Annual Society Award Winners 4 Jr Scientist activities 9 Mentor Mentee Lunch 10 New initiatives 12 Desk of our CEO, James McSwiggen 15 New Volunteer Opportunities 16 Chair, Meetings Committee, Benoit Chabot 17 Desk of the Membership Chair, Kristian Baker 18 Thank you Volunteers! 20 Meeting Reports: RNA Sponsored Meetings 22 Upcoming Meetings of Interest 27 Employment 31 1 Although the graceful city of Kyoto and its cultural months. First, in May 2016, the RNA journal treasures beckoned from just beyond the convention instituted a uniform price for manuscript publication hall, the meeting itself held more than enough (see p 12) that simplifies the calculation of author excitement to keep ones attention! Both the quality fees and facilitates the use of color figures to and the “polish” of the scientific presentations were convey scientific information.
    [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]
  • 2015 Wattiezm Memoire
    Institutional Repository - Research Portal Dépôt Institutionnel - Portail de la Recherche University of Namurresearchportal.unamur.be THESIS / THÈSE MASTER IN COMPUTER SCIENCE Design of a support system for modelling gene regulatory networks Author(s) - Auteur(s) : Wattiez, Morgan Award date: 2015 Awarding institution: University of Namur Supervisor - Co-Supervisor / Promoteur - Co-Promoteur : Link to publication Publication date - Date de publication : Permanent link - Permalien : Rights / License - Licence de droit d’auteur : General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. BibliothèqueDownload date: Universitaire 04. oct.. 2021 Moretus Plantin University of Namur Faculty of Computer Science Academic Year 2014{2015 Design of a support system for modelling gene regulatory networks Morgan WATTIEZ Supervisor: (Signed for Release Approval Jean-Marie JACQUET Study Rules art. 40) Thesis submitted in partial fulfillment of the requirements for the degree of Master in Computer Science at the University of Namur Abstract The understanding of gene regulatory networks depends upon the solving of ques- tions related to the interactions in those networks.
    [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]