DOCSLIB.ORG

Attendees List (As of December 18, 2012)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Attendees List (As of December 18, 2012) PSB 2013 Attendees List (as of December 18, 2012) Badri Adhikari Ingrid Borecki University of Missouri-Columbia Washington University Russ Altman Jonathan Boston Stanford University Vanderbilt University Folkert Asselbergs Steven Brenner University Medical Center Utrecht University of California, Berkeley Ferhat Ay Raquel Bromberg University of Washington University of Texas Southwestern Medical Center at Dallas Maulana Bachtiar National University of Singapore Yana Bromberg Rutgers University Pierre Baldi University of California Daniel Brown University of Waterloo Nuno Barbosa-Morais University of Toronto Jonathan Buckley USC Serge Batalov Novartis (GNF) Mark Burstein SIFT Md Shamsuzzoha Bayzid University of Texas at Austin William Bush Vanderbilt University Gurkan Bebek Case Western Reserve University Carlos Bustamante Stanford University Rotem Ben-Hamo Bar-Ilan University Atul Butte Stanford University Takis Benos University of Pittsburgh Julián Candia University of Maryland Jong Bhak Genome Research Foundation Renzhi Cao University of Missouri-Columbia Debswapna Bhattacharya University of Missouri-columbia Vincent Carey Harvard Medical School Surojit Biswas University of North Carolina at Chapel Hill Mark Chance Case Western Reserve University Marco Blanchette Stowers Institute For Medical Research Zhihua Chen H. Lee Moffitt Cancer Center & Research Institute Ben Blencowe University of Toronto Jie Cheng GlaxoSmithKline Anthony Bonner University of Toronto 1 PSB 2013 Attendees List (as of December 18, 2012) Chao Cheng Kenji Etchuya Dratmouth College Meiji University Jianlin Cheng Ramon Felciano University of Missouri, Columbia Ingenuity Systems Rosaria Chiang Doug Fenger Stanford University Dart NeuroScience Sun Choi Aris Floratos Ewha Womans University Columbia University Seungjin Choi Samuel Flores POSTECH Uppsala University Kevin Bretonnel Cohen Jeffrey Foster U. Colorado School of Medicine University of Maryland, College Park Dana Crawford James Foster Vanderbilt University University of Idaho Xuan Tho Dang Patricia Francis-Lyon Graduate School of Natural Science and Technology, University of San Francisco Kanazawa University Alex Frase James Degnan The Pennsylvania State University University of Canterbury Terry Gaasterland Scott Delp UC San Diego Stanford University Haitham Gabr Xin Deng University of Florida University Of Missouri-Columbia Richard Gayle Amanda Dick SpreadingScience University of Connecticut Olivier Gevaert Valentin Dinu Stanford University Arizona State University Robert Goodloe Adrian Dobra Vanderbilt University University of Washington Gregory Greant Scott Dudek University of Pennsylvania Pennsylvania State University Casey Greene David Ewing Duncan Geisel School of Medicine at Dartmouth A. Keith Dunker Anna Greene Indiana University Dartmouth College Howard Edenberg Indiana University 2 PSB 2013 Attendees List (as of December 18, 2012) David Haussler Maricel Kann Howard Hughes Medical Institute, University of University of Maryland, Baltimore County California Santa Cruz Rachel Karchin Katharina Hayer Johns Hopkins University University of Pennsylvania Konrad Karczewski David Heckerman Stanford University Microsoft Peter Kasson Benjamin Hitz University of Virginia Stanford University Manolis Kellis Yoshiyuki Hizukuri MIT Asubio Pharma Co., Ltd. Raya Khanin James Hoffman Memorial Sloan Kettering Cancer Center St. Jude Children's Research Hospital Docyong Kim Emily Holzinger Vanderbilt University Hiroaki Kitano Okinawa Institute of Science and Technology Haiyan Hu Graduate School University of Central Florida Teri Klein Ting Hu Stanford University Dartmouth College Daniel Klein Jing Hu Stanford University Franklin & Marshall College Kai Kohlhoff Grace Huang Google University of Pittsburgh/ Joint CMU-U Pitt PhD Program in Computational Biology Artemy Kolchinsky Indiana University Lawrence Hunter University of Colorado at Denver David Konerding Google Inc Suresh Jagannathan Purdue University Naama Kopelman Tel Aviv University Michael Januszyk Stanford University Mehmet Koyuturk Case Western Reserve University Vladimir Jojic University of North Carolina at Chapel Hill Azra Krek Memorial Sloan-Kettering Cancer Center Michael Jones Novartis Institutes for Biomedical Research Thomas Lasko Vanderbilt University School of Medicine Vivek Kaimal Regulus Therapeutics Tu Kien T. Le Kanazawa University 3 PSB 2013 Attendees List (as of December 18, 2012) Jong-Eun Lee Jason Moore DNA Link, Inc. Dartmouth College Joslynn Lee Carrie Moore Northeastern University Pennsylvania State University Sanghyuk Lee Martin Morgan Ewha Womans University Fred Hutchinson Cancer Research Center Hyunju Lee Quaid Morris Gwangju Institute of Science and Technology University of Toronto Li Li Yuri Mukai Stanford University Meiji University Xiaoman Li Luay Nakhleh University of Central Florida Rice University Xiaoping Liao Ryohei Nambu University of Alberta Meiji University Yu Lin Faisal Naqib EPFL McGill University Erik Lindahl Lan Anh T. Nguyen KTH Royal Institute of Technology Graduate School of Natural Science and Technology, Kanazawa University Jennifer Listgarten Microsoft Research William Noble University of Washington Tianyun Liu Stanford University Layla Oesper Brown University Zhiyong Lu NCBI/NLM Lawrence Oloff SOAR Kaixuan Luo Duke University Zbyszek Otwinowski University of Texas Southwestern Medical Center at Gabor Marth Dallas Boston College David Padua Richard McEachin University of Illinois at Urbana-Champaign Univ. Michigan Zheng Pan Jackson Miller Amgen Buck Institute for Research on Aging Kyunghyun Park Siavash Mir Arabbaygi PhD student University of Texas at Austin Jyotishman Pathak Sean Mooney Mayo Clinic Buck Institute for Research on Aging 4 PSB 2013 Attendees List (as of December 18, 2012) Kristine Pattin David Rocke Dartmouth College University of California, Davis Dana Pe'er Dan Roden Columbia University Vanderbilt University Itsik Pe'er Kyoungmin Roh Columbia University UCSB Matteo Pellegrini Thammakorn Saethang UCLA Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan Sarah Pendergrass The Pennsylvania State University Avinash Sahu University of of Maryland Alan Perez-Rathke University of Illinois at Chicago Rickard Sandberg Karolinska Institutet Stephen Piccolo University of Utah Kenji Satou Kanazawa University Sylvia Plevritis Stanford University Steve Scherer Baylor College of Medicine Aleksander Popel Johns Hopkins University Daniel Schrider Indiana University Nathan Price Instittute for Systems Biology Erick Scott The Scripps Research Institute Michael Province Washington University Shefali Setia The Pennsylvania state university Teresa Przytycka NCBI / NIH Sohrab Shah University of BC/BC Cancer Agency Frank Pugh Penn State University James Sikela University of Colorado Denver Xiang Qin Baylor College of Medicine Mona Singh Princeton University Joanna Raczynska University of Texas Southwestern Medical Center at Jennifer Smith Dallas Boise State University Sebastien Roch Michael Snyder UW-Madison Stanford University Luis Rocha Victor Solovyev Indiana University Softberry Inc. 5 PSB 2013 Attendees List (as of December 18, 2012) Paul Spellman Tao Wang Oregon Health & Science University Albert Einstein College of Medicine Oliver Stegle Zheng Wang EMBL-European Bioinformatics Institute University of Missouri Lars Steinmetz Tandy Warnow EMBL University of Texas Joshua Stuart Leor Weinberger UCSC Gladstone Institutes/UCSF Zhifu Sun Zhiping Weng Mayo Clinic University of Massachusetts Medical School Nicholas Tatonetti Ryan Whaley Columbia University PharmGKB - Stanford University Duncan Temple Lang Michelle Whirl-Carrillo UC Davis Stanford University Luke Tierney Scott Williams University of Iowa Dartmouth College Vu Anh Tran Mike Wong Graduate School of Natural Science and Technology, San Francisco State University Kanazawa University Yoshihiro Yamanishi Jakub Truszkowski Kyushu University University of Waterloo Shuxing Zhang Ozlem Uzuner MD Anderson Cancer Center Assistant Professor, University at Albany, SUNY Tao Zhong Ilya Vakser Fudan University University of Kansas Or Zuk Karin Verspoor Toyota Technological Institute at the University of National ICT Australia Chicago Jan Vitek Purdue University Olga Vitek Purdue University Randy Wadkins University of Mississippi Wenhui Wang Case Western Reserve University 6 .
Load more

Recommended publications
  • 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]
  • 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]
  • 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]
  • 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]
  • ABSTRACT HISTORICAL GRAPH DATA MANAGEMENT Udayan
    ABSTRACT Title of dissertation: HISTORICAL GRAPH DATA MANAGEMENT Udayan Khurana, Doctor of Philosophy, 2015 Dissertation directed by: Professor Amol Deshpande Department of Computer Science Over the last decade, we have witnessed an increasing interest in temporal analysis of information networks such as social networks or citation networks. Finding temporal interaction patterns, visualizing the evolution of graph properties, or even simply com- paring them across time, has proven to add significant value in reasoning over networks. However, because of the lack of underlying data management support, much of the work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. Unfortunately, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, informa- tion diffusion, formation of online communities, and so on. In the absence of appropriate support, an analyst today has to manually navigate the added temporal complexity of large evolving graphs, making the process cumbersome and ineffective. In this dissertation, I address the key challenges in storing, retrieving, and analyzing large historical graphs. In the first part, I present DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compact recording of the historical information, and that supports efficient retrieval of historical graph snapshots. I present analytical models for estimating required storage space and snapshot retrieval times which aid in choosing the right parameters for a specific scenario. I also present optimizations such as partial materialization and columnar storage to speed up snapshot retrieval. In the second part, I present Temporal Graph Index that builds upon DeltaGraph to support version-centric retrieval such as a node’s 1-hop neighborhood history, along with snapshot reconstruction.
    [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]
  • John Anthony Capra
    John Anthony Capra Contact Vanderbilt University e-mail: tony.capra-at-vanderbilt.edu Information Dept. of Biological Sciences www: http://www.capralab.org/ VU Station B, Box 35-1634 office: U5221 BSB/MRB III Nashville, TN 37235-1634 phone: (615) 343-3671 Research • Applying computational methods to problems in genetics, evolution, and biomedicine. Interests • Integrating genome-scale data to understand the functional effects of genetic differences between individuals and species. • Modeling evolutionary processes that drive the creation of lineage-specific traits and diseases. Academic Vanderbilt University, Nashville, Tennessee USA Employment Assistant Professor, Department of Biological Sciences August 2014 { Present Assistant Professor, Department of Biomedical Informatics February 2013 { Present Investigator, Center for Human Genetics Research Education And Gladstone Institutes, University of California, San Francisco, CA USA Training Postdoctoral Fellow, October 2009 { December 2012 • Advisor: Katherine Pollard Princeton University, Princeton, New Jersey USA Ph.D., Computer Science, June 2009 • Advisor: Mona Singh • Thesis: Algorithms for the Identification of Functional Sites in Proteins M.A., Computer Science, October 2006 Columbia College, Columbia University, New York, New York USA B.A., Computer Science, May 2004 B.A., Mathematics, May 2004 Pembroke College, Oxford University, Oxford, UK Columbia University Oxford Scholar, October 2002 { June 2003 • Subject: Mathematics Honors and Gladstone Institutes Award for Excellence in Scientific Leadership 2012 Awards Society for Molecular Biology and Evolution (SMBE) Travel Award 2012 PhRMA Foundation Postdoctoral Fellowship in Informatics 2011 { 2013 Princeton University Wu Graduate Fellowship 2004 { 2008 Columbia University Oxford Scholar 2002 { 2003 Publications Capra JA* and Kostka D*. Modeling DNA methylation dynamics with approaches from phyloge- netics.
    [Show full text]
  • 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.
    [Show full text]
  • Downloaded from [16, 18]
    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................................
    [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]