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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

JOURNAL OF CHEMINFORMATICS 2016年第SUPPL 1期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7-9 60487 Frankfurt Germany. ufechner@beilstein-institut.de. Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands. Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. stefan.guessregen@***. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. GlaxoSmithKline Stevenage SG1 2NY UK. nicola.j.richmond@***. Discngine Paris 75011 France. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. marwin.segler@wwu.de. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. shea@chem.ucsb.edu. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. Chemistry Department Ludwig-Maximilians-Universität München Butenandtstr. 7 81377 Munich Germany. Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany. CAM-D Technologies Essen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. achim.zielesny@w-hs.de. Leiden University Leiden Netherlands. j.fraaije@chem.leidenuniv.nl. Culgi BV Berlin Germany. Physikalische Chemie III TU Dortmund 44227 Dortmund Germany. stefan.kast@tu-dortmund.de. Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom. kcb27@cam.ac.uk. Centre for Molecular Informatics Department of Chemistry

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Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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Scientific data 2017年第1期4卷 170179页

作者： Jason Flannick Christian Fuchsberger Anubha Mahajan Tanya M Teslovich Vineeta Agarwala Kyle J Gaulton Lizz Caulkins Ryan Koesterer Clement Ma Loukas Moutsianas Davis J McCarthy Manuel A Rivas John R B Perry Xueling Sim Thomas W Blackwell Neil R Robertson N William Rayner Pablo Cingolani Adam E Locke Juan Fernandez Tajes Heather M Highland Josee Dupuis Peter S Chines Cecilia M Lindgren Christopher Hartl Anne U Jackson Han Chen Jeroen R Huyghe Martijn van de Bunt Richard D Pearson Ashish Kumar Martina Müller-Nurasyid Niels Grarup Heather M Stringham Eric R Gamazon Jaehoon Lee Yuhui Chen Robert A Scott Jennifer E Below Peng Chen Jinyan Huang Min Jin Go Michael L Stitzel Dorota Pasko Stephen C J Parker Tibor V Varga Todd Green Nicola L Beer Aaron G Day-Williams Teresa Ferreira Tasha Fingerlin Momoko Horikoshi Cheng Hu Iksoo Huh Mohammad Kamran Ikram Bong-Jo Kim Yongkang Kim Young Jin Kim Min-Seok Kwon Juyoung Lee Selyeong Lee Keng-Han Lin Taylor J Maxwell Yoshihiko Nagai Xu Wang Ryan P Welch Joon Yoon Weihua Zhang Nir Barzilai Benjamin F Voight Bok-Ghee Han Christopher P Jenkinson Teemu Kuulasmaa Johanna Kuusisto Alisa Manning Maggie C Y Ng Nicholette D Palmer Beverley Balkau Alena Stančáková Hanna E Abboud Heiner Boeing Vilmantas Giedraitis Dorairaj Prabhakaran Omri Gottesman James Scott Jason Carey Phoenix Kwan George Grant Joshua D Smith Benjamin M Neale Shaun Purcell Adam S Butterworth Joanna M M Howson Heung Man Lee Yingchang Lu Soo-Heon Kwak Wei Zhao John Danesh Vincent K L Lam Kyong Soo Park Danish Saleheen Wing Yee So Claudia H T Tam Uzma Afzal David Aguilar Rector Arya Tin Aung Edmund Chan Carmen Navarro Ching-Yu Cheng Domenico Palli Adolfo Correa Joanne E Curran Dennis Rybin Vidya S Farook Sharon P Fowler Barry I Freedman Michael Griswold Daniel Esten Hale Pamela J Hicks Chiea-Chuen Khor Satish Kumar Benjamin Lehne Dorothée Thuillier Wei Yen Lim Jianjun Liu Marie Loh Solomon K Musani Sobha Puppala William R Scott Loïc Yengo Sian-Tsung Tan Herman A Taylor Farook Thameem Gregory Wilson Tien Yin Wong Pål Rasmu Department of Molecular Biology Massachusetts General Hospital Boston Massachusetts USA. Program in Medical and Population Genetics Broad Institute Cambridge Massachusetts USA. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor Michigan USA. Wellcome Trust Centre for Human Genetics Nuffield Department of Medicine University of Oxford Oxford UK. Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Statistics University of Oxford Oxford UK. Genetics of Complex Traits University of Exeter Medical School University of Exeter Exeter UK. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Department of Twin Research and Genetic Epidemiology King's College London London UK. Oxford Centre for Diabetes Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK. Department of Human Genetics Wellcome Trust Sanger Institute Hinxton Cambridgeshire UK. School of Computer Science McGill University Montreal Quebec Canada. McGill University and Génome Québec Innovation Centre Montreal Quebec Canada. Human Genetics Center The University of Texas Graduate School of Biomedical Sciences at Houston The University of Texas Health Science Center at Houston Houston Texas USA. Department of Biostatistics Boston University School of Public Health Boston Massachusetts USA. National Heart Lung and Blood Institute's Framingham Heart Study Framingham Massachusetts USA. Medical Genomics and Metabolic Genetics Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland USA. Department of Biostatistics Harvard School of Public Health Boston Massachusetts USA. Chronic Disease Epidemiology Swiss Tropical and Public Health Institute University of Basel Basel Switzerland. Institute of Genetic Epidemiology Helmholtz Zentrum München German R

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

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BMC bioinformatics 2016年第3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca USA Graduate Field of Computational Biology Cornell University Ithaca USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Antwerpen Belgium Department of Computer Science University of Arizona Tucson USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Minds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai India Philips Research North America Briarcliff Manor USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Tokyo Japan Systems Biology Centre University of Warwick Senate House Coventry UK Systems Biology Doctoral Training Centre University of Warwick Senate House Coventry UK Center for B

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

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CORRIGENDUM: "A visualization for the dynamic behaviors of the mixture of water mass for northwestern pacific near Japan"

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International Journal of Modeling, Simulation, and Scientific Computing 2015年第2期6卷

作者： Kun Zhao Satoshi Nakada Naohisa Sakamoto Koji Koyamada Chandrajit Bajaj Yoichi Ishikawa Toshiyuki Awaji Teiji In Sei-Ichi Saitoh Graduate School of Engineering Kyoto University Kyoto Kyoto Prefecture 606-8501 Japan Institute for the Promotion of Excellence in Higher Education Kyoto University Kyoto Kyoto Prefecture 606-8501 Japan Department of Computer Science The Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas 78712 USA Department of Geophysics Division of Earth and Planetary Sciences Kyoto University Kyoto Kyoto Prefecture 606-8501 Japan Data Research Center for Marine-Earth Sciences Japan Agency for Marine-Earth Science and Technology Yokosuka Kanakawa Prefecture 237-0061 Japan Japan Marine Science Foundation Tokyo Tokyo Metropolis 110-0008 Japan Laboratory of Marine Bioresource and Environment Sensing Faculty of Fisheries Sciences Hokkaido University Sapporo Hokkaido Prefecture 060-0808 Japan

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Opposites attract: computational and quantitative outreach through artistic expressions 13

Opposites attract: Computational and quantitative outreach t...

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Conference on Extreme science and engineering Discovery Environment, XSEDE 2013

作者： Szczepański, Amy F. Yost, Christal Magden, Norman Meaney, Evan Staples, Carolyn I. Remote Data Analysis and Visualization Center Electrical Engineering and Computer Science University of Tennessee United States National Institute for Computational Sciences University of Tennessee United States School of Art University of Tennessee United States

ISBN: (纸本)9781450321709

Staff from the University of Tennessee's Joint institute for computational sciences, National Institute for computational sciences, and Remote data Analysis and Visualization center have teamed up with faculty from UT's School of Art to engage with students, the public, and the research community on a number of projects that connect the arts with the science and computing disciplines. These collaborations have led to coursework for students, videos about scientific discovery, and the production of novel, computer- mediated artwork. Both the arts and the sciences have gained from these collaborations. © 2013 by the Association for Computing Machinery, Inc.

关键词： Visualization

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The open connectome project data cluster: Scalable analysis and vision for high-throughput neuroscience

The open connectome project data cluster: Scalable analysis ...

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25th International Conference on Scientific and Statistical database Management, SSDBM 2013

作者： Burns, Randal Roncal, William Gray Kleissas, Dean Lillaney, Kunal Manavalan, Priya Perlman, Eric Berger, Daniel R. Bock, Davi D. Chung, Kwanghun Grosenick, Logan Kasthuri, Narayanan Weiler, Nicholas C. Deisseroth, Karl Kazhdan, Michael Lichtman, Jeff Reid, R. Clay Smith, Stephen J. Szalay, Alexander S. Vogelstein, Joshua T. Vogelstein, R. Jacob Department of Computer Science Institute for Data Intensive Engineering and Science Johns Hopkins University United States Applied Physics Laboratory Johns Hopkins University United States Department of Statistical Science and Mathematics Institute for Brain Science Duke University United States Janelia Farm Research Campus Howard Hughes Medical Institute United States Department of Molecular and Cellular Biology Harvard University United States Department of Computational Neuroscience Massachusetts Institute of Technology United States Allen Institute for Brain Science United States Department of Physics and Astronomy Institute for Data Intensive Engineering and Science Johns Hopkins University United States Department of Bioengineering Stanford University United States Department of Molecular and Cellular Physiology Stan- Ford University United States

ISBN: (纸本)9781450319218

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at ***. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effectiveness of spatial data organization. Copyright © 2013 ACM.

关键词： Digital storage

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A VASUALIZATION FOR THE DYNAMIC BEHAVIORS OF THE MIXTURE OF WATER MASS FOR NORTHWESTERN PACIFIC NEAR JAPAN

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International Journal of Modeling, Simulation, and Scientific Computing 2013年第S01期4卷 86-103页

作者： KUN ZHAO SATOSHI NAKADA NAOHISA SAKAMOTO KOJI KOYAMADA CHANDRAJIT BAJAJ YOICHI ISHIKAWA TOSHIYUKI AWAJI TEIJI IN SEI-ICHI SAITOH Graduate School of Engineering Kyoto University KyotoKyoto Prefecture606-8501Japan Institute for the Promotion of Excellence in Higher Education Kyoto University KyotoKyoto Prefecture606-8501Japan Department of Computer Science The Institute for Computational Engineering and Sciences University of Texas at Austin AustinTexas78712USA Department of Geophysics Division of Earth and Planetary Sciences Kyoto University KyotoKyoto Prefecture606-8501Japan Data Research Center for Marine-Earth Sciences Japan Agency for Marine-Earth Science and Technology Yokosuka Kanakawa Prefecture237-0061Japan Japan Marine Science Foundation Tokyo Tokyo Metropolis110-0008Japan Laboratory of Marine Bioresource and Environment Sensing Faculty of Fisheries Sciences Hokkaido University SapporoHokkaido Prefecture060-0808Japan

Recent studies are focusing on the distribution of water mass because the mixture region of water mass is highly related to the rich fishing grounds[Yasuda I.,Watanabe Y.,***.3(3):172–181,1994].Due to the large data size and time-varying property,efficient exploration and visualization of the ocean data is always extremely *** extract the dynamic behaviors of the water mass and its mixture from a large-scale simulated ocean dataset,we developed an efficient visualization system by applying our volume compression method and our volume rendering *** system allows us to investigate the time-varying distributions of ocean physical properties,additionally from the user’s perspective and *** the experiments,we show the generality and expressiveness by applying our system for single-and multi-property visualizations to find some significant ocean water ***,we could obtain a clear visualization result to show the dynamic behaviors of the mixture of water mass for simulation data regarding a location in the northwestern Pacific near Japan.

关键词： Oceanography water mass visualization.

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A tale of two systems: Flexibility of usage of Kraken and Nautilus at the National Institute for computational sciences

A tale of two systems: Flexibility of usage of Kraken and Na...

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1st Conference of the Extreme science and engineering Discovery Environment: Bridging from the eXtreme to the Campus and Beyond, XSEDE12

作者： Szczepański, Amy F. Huang, Jian Ahern, Sean Fahey, Mark R. Remote Data Analysis and Visualization Center Electrical Engineering and Computer Science University of Tennessee 1520 Middle Drive Knoxville TN 37996-2250 United States Remote Data Analysis and Visualization Center National Institute for Computational Sciences University of Tennessee PO Box 2008 Oak Ridge TN 37831-6173 United States National Institute for Computational Sciences Industrial and Information Engineering University of Tennessee PO Box 2008 Oak Ridge TN 37831-6173 United States

ISBN: (纸本)9781450316026

The National Institute for computational sciences (NICS) at the University of Tennessee currently operates two computational resources for the eXtreme science and engineering Discovery Environment (XSEDE), Kraken, a 112,896-core Cray XT5 for general purpose computation, and Nautilus, a 1,024-core SGI Altix UV 1000 for data analysis and visualization. We analyze a year's worth of accounting logs for Kraken and Nautilus to understand how users take advantage of these two systems and how analysis jobs differ from general HPC computation We find that researchers take advantage of the flexibility offered by these systems, running a wide variety of jobs at many scales and using the full range of core counts and available memory for their jobs. The jobs on Nautilus tend to use less walltime and more memory per core than the jobs run on Kraken. Additionally, researchers are more likely to run interactive jobs on Nautilus than on Kraken. Small jobs experience a good quality of service on both systems. This information can be used for the management and allocation of time on existing HPC and analysis systems as well as for planning for deploying future HPC and analysis systems. © 2012 ACM.

关键词： data visualization

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A quantitative evaluation of techniques for detection of abnormal change events in blogs. 12

A quantitative evaluation of techniques for detection of abn...

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12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12

作者： Bogen II, Paul L. Furuta, Richard Shipman, Frank Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge TN United States Center for the Study of Digital Libraries Dept. of Computer Science and Engineering Texas A and M University College Station TX 77843 United States

ISBN: (纸本)9781450311540

While most digital collections have limited forms of change - primarily creation and deletion of additional resources - there exists a class of digital collections that undergoes additional kinds of change. These collections are made up of resources that are distributed across the Internet and brought together into a collection via hyperlinking. Resources in these collections can be expected to change as time goes on. Part of the difficulty in maintaining these collections is determining if a changed page is still a valid member of the collection. Others have tried to address this problem by measuring change and defining a maximum allowed threshold of change, however, these methods treat all change as a potential problem and treat web content as a static document despite its intrinsically dynamic nature. Instead, we approach the significance of change on the web as a normal part of a web document's life-cycle and determine the difference between what a maintainer expects a page to do and what it actually does. In this work we evaluate the different options for extractors and analyzers in order to determine the best options from a suite of techniques. The evaluation used a human-generated ground-truth set of blog changes. The results of this work showed a statistically significant improvement over a range of traditional threshold techniques when applied to our collection of tagged blog changes. © 2012 ACM.

关键词： Pattern recognition

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Collaborative authoring of Walden's paths

Collaborative authoring of Walden's paths

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2nd International Conference on Theory and Practice of Digital Libraries, TPDL 2012

作者： Li, Yuanling Bogen II, Paul Logasa Pogue, Daniel Furuta, Richard Shipman, Frank Center for the Study of Digital Libraries Department of Computer Science and Engineering Texas A and M University College Station TX United States Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge United States User Experience and Interaction Design Team Production Enhancement Halliburton Energy Services Houston TX United States

ISBN: (纸本)9783642332890

This paper presents a prototype of an authoring tool to allow users to collaboratively build, annotate, manage, share and reuse collections of distributed resources from the World Wide Web. This extends on the Walden's Path project's work to help educators bring resources found on the World Wide Web into a linear contextualized structure. The introduction of collaborative authoring feature fosters collaborative learning activities through social interaction among participants, where participants can coauthor paths in groups. Besides, the prototype supports path sharing, branching and reusing;specifically, individual participant can contribute to the group with private collections of knowledge resources;paths completed by group can be shared among group members, such that participants can tailor, extend, reorder and/or replace nodes to have sub versions of shared paths for different information needs. © 2012 Springer-Verlag.

关键词： World Wide Web

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