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

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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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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Joint Analysis of BICEP2/Keck Array and Planck data

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Physical Review Letters 2015年第10期114卷 101301-101301页

作者： P. A. R. Ade N. Aghanim Z. Ahmed R. W. Aikin K. D. Alexander M. Arnaud J. Aumont C. Baccigalupi A. J. Banday D. Barkats R. B. Barreiro J. G. Bartlett N. Bartolo E. Battaner K. Benabed A. Benoît A. Benoit-Lévy S. J. Benton J.-P. Bernard M. Bersanelli P. Bielewicz C. A. Bischoff J. J. Bock A. Bonaldi L. Bonavera J. R. Bond J. Borrill F. R. Bouchet F. Boulanger J. A. Brevik M. Bucher I. Buder E. Bullock C. Burigana R. C. Butler V. Buza E. Calabrese J.-F. Cardoso A. Catalano A. Challinor R.-R. Chary H. C. Chiang P. R. Christensen L. P. L. Colombo C. Combet J. Connors F. Couchot A. Coulais B. P. Crill A. Curto F. Cuttaia L. Danese R. D. Davies R. J. Davis P. de Bernardis A. de Rosa G. de Zotti J. Delabrouille J.-M. Delouis F.-X. Désert C. Dickinson J. M. Diego H. Dole S. Donzelli O. Doré M. Douspis C. D. Dowell L. Duband A. Ducout J. Dunkley X. Dupac C. Dvorkin G. Efstathiou F. Elsner T. A. Enßlin H. K. Eriksen E. Falgarone J. P. Filippini F. Finelli S. Fliescher O. Forni M. Frailis A. A. Fraisse E. Franceschi A. Frejsel S. Galeotta S. Galli K. Ganga T. Ghosh M. Giard E. Gjerløw S. R. Golwala J. González-Nuevo K. M. Górski S. Gratton A. Gregorio A. Gruppuso J. E. Gudmundsson M. Halpern F. K. Hansen D. Hanson D. L. Harrison M. Hasselfield G. Helou S. Henrot-Versillé D. Herranz S. R. Hildebrandt G. C. Hilton E. Hivon M. Hobson W. A. Holmes W. Hovest V. V. Hristov K. M. Huffenberger H. Hui G. Hurier K. D. Irwin A. H. Jaffe T. R. Jaffe J. Jewell W. C. Jones M. Juvela A. Karakci K. S. Karkare J. P. Kaufman B. G. Keating S. Kefeli E. Keihänen S. A. Kernasovskiy R. Keskitalo T. S. Kisner R. Kneissl J. Knoche L. Knox J. M. Kovac N. Krachmalnicoff M. Kunz C. L. Kuo H. Kurki-Suonio G. Lagache A. Lähteenmäki J.-M. Lamarre A. Lasenby M. Lattanzi C. R. Lawrence E. M. Leitch R. Leonardi F. Levrier A. Lewis M. Liguori P. B. Lilje M. Linden-Vørnle M. López-Caniego P. M. Lubin M. Lueker J. F. Macías-Pérez B. Maffei D. Maino N. Mandolesi A. Mangilli M. Maris P. G. Martin E. Martínez-González S. Masi P. Mason S. Matarrese K. G. Megerian School of Physics and Astronomy Cardiff University Queens Buildings The Parade Cardiff CF24 3AA United Kingdom Institut d’Astrophysique Spatiale CNRS (UMR8617) Université Paris-Sud 11 Bâtiment 121 Orsay France Department of Physics Stanford University Stanford California 94305 USA California Institute of Technology Pasadena California USA Harvard-Smithsonian Center for Astrophysics 60 Garden Street MS 42 Cambridge Massachusetts 02138 USA Laboratoire AIM IRFU/Service d’Astrophysique–CEA/DSM–CNRS–Université Paris Diderot Bâtiment 709 CEA-Saclay F-91191 Gif-sur-Yvette Cedex France SISSA Astrophysics Sector via Bonomea 265 34136 Trieste Italy Université de Toulouse UPS-OMP IRAP F-31028 Toulouse cedex 4 France CNRS IRAP 9 Avenue colonel Roche BP 44346 F-31028 Toulouse cedex 4 France Joint ALMA Observatory Vitacura Santiago Chile Instituto de Física de Cantabria (CSIC-Universidad de Cantabria) Avenida de los Castros s/n Santander Spain APC AstroParticule et Cosmologie Université Paris Diderot CNRS/IN2P3 CEA/lrfu Observatoire de Paris Sorbonne Paris Cité 10 rue Alice Domon et Léonie Duquet 75205 Paris Cedex 13 France Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena California USA Dipartimento di Fisica e Astronomia G. Galilei Università degli Studi di Padova via Marzolo 8 35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova via Marzolo 8 I-35131 Padova Italy University of Granada Departamento de Física Teórica y del Cosmos Facultad de Ciencias Granada Spain University of Granada Instituto Carlos I de Física Teórica y Computacional Granada Spain Institut d’Astrophysique de Paris CNRS (UMR7095) 98 bis Boulevard Arago F-75014 Paris France UPMC Université de Paris 06 UMR7095 98 bis Boulevard Arago F-75014 Paris France Institut Néel CNRS Université Joseph Fourier Grenoble I 25 rue des Martyrs Grenoble France Department of Physics and Astronomy University College London L

We report the results of a joint analysis of data from BICEP2/Keck Array and Planck. BICEP2 and Keck Array have observed the same approximately 400 deg2 patch of sky centered on RA 0 h, Dec. −57.5°. The combined maps reach a depth of 57 nK deg in Stokes Q and U in a band centered at 150 GHz. Planck has observed the full sky in polarization at seven frequencies from 30 to 353 GHz, but much less deeply in any given region (1.2 μK deg in Q and U at 143 GHz). We detect 150×353 cross-correlation in B modes at high significance. We fit the single- and cross-frequency power spectra at frequencies ≥150 GHz to a lensed-ΛCDM model that includes dust and a possible contribution from inflationary gravitational waves (as parametrized by the tensor-to-scalar ratio r), using a prior on the frequency spectral behavior of polarized dust emission from previous Planck analysis of other regions of the sky. We find strong evidence for dust and no statistically significant evidence for tensor modes. We probe various model variations and extensions, including adding a synchrotron component in combination with lower frequency data, and find that these make little difference to the r constraint. Finally, we present an alternative analysis which is similar to a map-based cleaning of the dust contribution, and show that this gives similar constraints. The final result is expressed as a likelihood curve for r, and yields an upper limit r0.05<0.12 at 95% confidence. Marginalizing over dust and r, lensing B modes are detected at 7.0σ significance.

关键词： STOKES equations FREQUENCIES of oscillating systems POWER spectra GRAVITATIONAL waves

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Verification of adaptive systems

Verification of adaptive systems

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AIAA Infotech at Aerospace Conference and Exhibit 2012

作者： Pullum, Laura Cui, Xiaohui Vassev, Emil Hinchey, Mike Rouff, Christopher Buskens, Richard Computational Data Analytics Group Oak Ridge National Laboratory P.O. Box 2008 MS-6085 Oak Ridge TN 37830 United States Computer Science Department New York Institute of Technology 1855 Broadway Manhattan NY 10023 United States Lero - The Irish Software Engineering Research Centre University of Limerick Limerick Ireland Lockheed Martin Advanced Technology Laboratories Informatics Laboratory 3 Executive Campus Suite 600 Cherry Hill NJ 08002 United States Lockheed Martin Advanced Technology Laboratories Informatics Laboratory Applied Science Laboratory 3 Executive Campus Cherry Hill NJ 08002 United States

ISBN: (数字)9781600869396

ISBN: (纸本)9781600869396

Adaptive systems are critical for future space and other unmanned and intelligent systems. Verification of these systems is also critical for their use in systems with potential harm to human life or with large financial investments. Due to their nondeterministic nature and extremely large state space, current methods for verification of software systems are not adequate to provide a high level of assurance for them. The combination of stabilization science, high performance computing simulations, compositional verification and traditional verification techniques, plus operational monitors, provides a complete approach to verification and deployment of adaptive systems that has not been used before. This paper gives an overview of this approach. © 2012 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Adaptive systems

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Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models

Managing dynamic enterprise and urgent workloads on clouds u...

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作者： Bacigalupo, David A. Van Hemert, Jano Chen, Xiaoyu Usmani, Asif Chester, Adam P. He, Ligang Dillenberger, Donna N. Wills, Gary B. Gilbert, Lester Jarvis, Stephen A. School of Electronics and Computer Science University of Southampton United Kingdom Data-Intensive Research Group School of Informatics University of Edinburgh United Kingdom BRE Centre for Fire Safety Engineering School of Engineering University of Edinburgh United Kingdom IBM T.J. Watson Research Centre Yorktown Heights NY United States High Performance Systems Group Department of Computer Science University of Warwick United Kingdom

The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: (i) comparatively evaluate the layered queuing and historical techniques;(ii) evaluate the effectiveness of the management algorithm in different operating scenarios;and (iii) provide guidance on using prediction-based workload and resource management. © 2011 Elsevier B.V. All rights reserved.

关键词： Resource allocation

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Resource management of enterprise cloud systems using layered queuing and historical performance models

Resource management of enterprise cloud systems using layere...

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2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010

作者： Bacigalupo, David A. Van Hemert, Jano Usmani, Asif Dillenberger, Donna N. Wills, Gary B. Jarvis, Stephen A. School of Electronics and Computer Science University of Southampton SO17 1BJ United Kingdom Data-Intensive Research Group School of Informatics University of Edinburgh EH8 9AB United Kingdom BRE Centre for Fire Safety Engineering School of Engineering University of Edinburgh EH9 3JL United Kingdom IBM T.J. Watson Research Centre Yorktown Heights NY 10598 United States Department of Computer Science High Performance Systems Group University of Warwick CV4 7AL United Kingdom

ISBN: (纸本)9781424465347

The automatic allocation of enterprise workload to resources can be enhanced by being able to make 'whatif' response time predictions, whilst different allocations are being considered. It is important to quantitatively compare the effectiveness of different prediction techniques for use in cloud infrastructures. To help make the comparison of relevance to a wide range of possible cloud environments it is useful to consider the following. 1.) urgent cloud customers such as the emergency services that can demand cloud resources at short notice (e.g. for our FireGrid emergency response software). 2.) dynamic enterprise systems, that must rapidly adapt to frequent changes in workload, system configuration and/or available cloud servers. 3.) The use of the predictions in a coordinated manner by both the cloud infrastructure and cloud customer management systems. 4.) A broad range of criteria for evaluating each technique. However, there have been no previous comparisons meeting these requirements. This paper, meeting the above requirements, quantitatively compares the layered queuing and ("HYDRA") historical techniques - including our initial thoughts on how they could be combined. Supporting results and experiments include the following: i.) defining, investigating and hence providing guidelines on the use of a historical and layered queuing model;ii.) using these guidelines showing that both techniques can make low overhead and typically over 70% accurate predictions, for new server architectures for which only a small number of benchmarks have been run;and iii.) defining and investigating tuning a prediction-based cloud workload and resource management algorithm. © 2010 IEEE.

关键词： Forecasting

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Resource management of enterprise cloud systems using layered queuing and historical performance models

Resource management of enterprise cloud systems using layere...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David A. Bacigalupo Jano van Hemert Asif Usmani Donna N. Dillenberger Gary B. Wills Stephen A. Jarvis School of Electronics and Computer Science University of Southampton UK Data-Intensive Research Group School of Informatics University of Edinburgh UK BRE Centre of Fire Safety Engineering School of Engineering University of Edinburgh UK IBM Thomas J. Watson Research Center Yorktown Heights NY USA High Performance Systems Group Department of Computer Science University of Warwick UK

ISBN: (纸本)9781424465330

The automatic allocation of enterprise workload to resources can be enhanced by being able to make `what-if' response time predictions, whilst different allocations are being considered. It is important to quantitatively compare the effectiveness of different prediction techniques for use in cloud infrastructures. To help make the comparison of relevance to a wide range of possible cloud environments it is useful to consider the following. 1.) urgent cloud customers such as the emergency services that can demand cloud resources at short notice (e.g. for our FireGrid emergency response software). 2.) dynamic enterprise systems, that must rapidly adapt to frequent changes in workload, system configuration and/or available cloud servers. 3.) The use of the predictions in a coordinated manner by both the cloud infrastructure and cloud customer management systems. 4.) A broad range of criteria for evaluating each technique. However, there have been no previous comparisons meeting these requirements. This paper, meeting the above requirements, quantitatively compares the layered queuing and (¿HYDRA¿) historical techniques - including our initial thoughts on how they could be combined. Supporting results and experiments include the following: i.) defining, investigating and hence providing guidelines on the use of a historical and layered queuing model; ii.) using these guidelines showing that both techniques can make low overhead and typically over 70% accurate predictions, for new server architectures for which only a small number of benchmarks have been run; and iii.) defining and investigating tuning a prediction-based cloud workload and resource management algorithm.

关键词： Resource management Clouds Delay Predictive models Computer science data engineering Emergency services Disaster management Guidelines Quality of service

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Prediction of protein sub-cellular localization using information from texts and sequences

Prediction of protein sub-cellular localization using inform...

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2008 Current Trends in Biomedical Natural Language Processing, BioNLP 2008

作者： Chun, Hong-Woo Yamasaki, Chisato Saichi, Naomi Tanaka, Masayuki Hishiki, Teruyoshi Imanishi, Tadashi Gojobori, Takashi Kim, Jin-Dong Tsujii, Jun'Ichi Takagi, Toshihisa Database Center for Life Science Research Organization of Information and System University of Tokyo Engineering 12th Bldg. 2-11-16 Yayoi Bunkyo-ku Tokyo113-0032 Japan Japan Biological Information Research Center Japan Biological Informatics Consortium Japan Biological Information Research Center National Institute of Advanced Industrial Science and Technology Japan Department of Computer Science University of Tokyo Japan Graduate School of Information Science and Technology Hokkaido University Japan Center for Information Biology DNA Data Bank of Japan National Institute of Genetics United Kingdom School of Informatics University of Manchester United Kingdom National Centre for Text Mining United Kingdom Department of Computational Biology University of Tokyo Japan

ISBN: (纸本)9781932432114

关键词： Proteins

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