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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Clustering patient medical records via sparse subspace representation

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17th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2013

作者： Saha, Budhaditya Pham, Duc-Son Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong VIC Australia Institute for Multi-sensor Processing and Content Analysis Department of Computing Curtin University WA Australia

ISBN: (纸本)9783642374555

The health industry is facing increasing challenge with "big data" as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of subspace clustering. Our novel contribution lies in the exploitation of sparse representation to discover subspaces automatically and a domain-specific construction of weighting matrices for patient records. We show the new formulation is readily solved by extending existing 1 -regularized optimization algorithms. Using a cohort of both diabetes and stroke data we show that we outperform existing benchmark clustering techniques in the literature. © Springer-Verlag 2013.

关键词： Clustering algorithms

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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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data Intensive computing of X-Ray Computed Tomography Reconstruction at the LSDF

Data Intensive Computing of X-Ray Computed Tomography Recons...

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Euromicro Conference on Parallel, Distributed and Network-Based processing

作者： Xiaoli Yang Thomas Jejkal Halil Pasic Rainer Stotzka Achim Streit Jos van Wezel Tomy dos Santos Rolo Institute for Data Processing and Electronics (IPE) Karlsruhe Institute of Technology Germany Steinbuch Centre for Computing (SCC) Karlsruhe Germany Institute for Synchrotron Radiation (ISS/ANKA) Karlsruhe Germany

In this paper, the method of data intensive computing is studied for large amounts of data in computed tomography (CT). An automatic workflow is built up to connect the tomography beamline of ANKA with the large scale data facility (LSDF), able to enhance the data storage and analysis efficiency. In this workflow, this paper focuses on the parallel computing of 3D computed tomography reconstruction. Different from the existing reconstruction system with filtered back-projection method, an algebraic reconstruction technique based on compressive sampling theory is presented to reconstruct the data from ultrafast computed tomography with fewer projections. Then the connected computing resources at the LSDF are used to implement the 3D CT reconstruction by distributing the whole job into multiple tasks executed in parallel. Promising reconstruction images and high computing performance are reported. For the 3D X-ray CT reconstruction, less than six minutes are actually required. LSDF is not only able to organize data efficiently, but also can provide reconstructed results to users in nearly instantaneous time. After integration into the workflow, this data intensive computing method will largely improve the data processing for ultrafast computed tomography at ANKA.

关键词： Image reconstruction Computed tomography Three-dimensional displays Parallel processing Subspace constraints Computers

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LAMBDA -- The LSDF Execution Framework for data Intensive Applications

LAMBDA -- The LSDF Execution Framework for Data Intensive Ap...

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Euromicro Conference on Parallel, Distributed and Network-Based processing

作者： Thomas Jejkal Volker Hartmann Rainer Stotzka Jens Otte Ariel Garcia Jos van Wezel Achim Streit Institute for Data Processing and Electronics (IPE) Karlsruhe Institute of Technology Karlsruhe Germany Institute of Toxicology and Genetics (ITG) Karlsruhe Institute of Technology Karlsruhe Germany Steinbuch Centre for Computing (SCC) Karlsruhe Institute of Technology Karlsruhe Germany

To cope with the growing requirements of data intensive scientific experiments, models and simulations the Large Scale data Facility (LSDF) at KIT aims to support many scientific disciplines. The LSDF is a distributed storage facility at Exabyte scale providing storage, archives, data bases and meta data repositories. Apart from data storage many scientific communities need to perform data processing operations as well. For this purpose the LSDF Execution Framework for data Intensive Applications (LAMBDA) was developed to allow asynchronous high-performance data processing next to the LSDF. However, it is not restricted to the LSDF or any special feature only available at the LSDF. The main goal of LAMBDA is to simplify large scale data processing for scientific users by reducing complexity, responsibility and error-proneness. The description of an execution is realized as part of LAMBDA administration in the background via meta data that can be obtained from arbitrary sources. Thus, the scientific user has only to select which applications he wants to apply to his data.

关键词： Software data processing Communities Microscopy Runtime Monitoring Reliability

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data-intensive analysis for scientific experiments at the large scale data facility

Data-intensive analysis for scientific experiments at the la...

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1st IEEE Symposium on Large-Scale data Analysis and Visualization 2011, LDAV 2011

作者： García, A.O. Bourov, S. Hammad, A. Hartmann, V. Jejkal, T. Otte, J.C. Pfeiffer, S. Schenker, T. Schmidt, C. Neuberger, P. Stotzka, R. Van Wezel, J. Neumair, B. Streit, A. Steinbuch Centre for Computing Karlsruhe Institute of Technology Karlsruhe Germany Institute for Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Toxicology and Genetics Karlsruhe Institute of Technology Karlsruhe Germany Institute for Applied Computer Science Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9781467301541

The Large Scale data Facility (LSDF) was conceived and launched at the Karlsruhe Institute of technology (KIT) end of 2009 to address the growing need for value-added storage services for data intensive experiments. The LSDF main focus is to support scientific experiments producing large data sets reaching into the petabyte range with adequate storage, support and value added services for data management, processing and preservation. In this work we describe the approach taken to perform data analysis in LSDF, as well as for data management of the scientific datasets. © 2011 IEEE.

关键词： Information management

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data management and analysis at the Large Scale data Facility

Data management and analysis at the Large Scale Data Facilit...

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2011 6th International Conference on Digital Information Management, ICDIM 2011

作者： García, A.O. Bourov, S. Hammad, A. Jejkal, T. Otte, J.C. Pfeiffer, S. Schenker, T. Schmidt, C. Van Wezel, J. Neumair, B. Streit, A. Karlsruhe Institute of Technology Steinbuch Centre for Computing Karlsruhe Germany Karlsruhe Institute of Technology Institute of Toxicology and Genetics Karlsruhe Germany Karlsruhe Institute of Technology Institute for Applied Computer Science Karlsruhe Germany Karlsruhe Institute of Technology Institute for Data Processing and Electronics Karlsruhe Germany

ISBN: (纸本)9781457715389

The Large Scale data Facility (LSDF) was started at the Karlsruhe Institute of technology (KIT) end of 2009 to address the growing need for value-added storage services for its data intensive experiments. The main focus of the project is to provide scientific communities producing data collections in the tera to petabyte range with the necessary hardware infrastructure as well as with adequate value-added services and support for the data management, processing, and preservation. In this work we describe the project's infrastructure and services design, as well as its meta data handling. Both community specific meta data schemes, a meta data repository, an application programming interface and a graphical tool for accessing the resources were developed to further support the processing workflows of the partner scientific communities. The analysis workflow of high throughput microscopy images for studying biomedical processes is described in detail. © 2011 IEEE.

关键词： Information management

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The Large Scale data Facility: data Intensive computing for Scientific Experiments

The Large Scale Data Facility: Data Intensive Computing for ...

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

作者： Ariel O. Garcia Serguei Bourov Ahmad Hammad Jos van Wezel Bernhard Neumair Achim Streit Volker Hartmann Thomas Jejkal Patrick Neuberger Rainer Stotzka Steinbuch Centre of Computing Karlsruhe Institute of Technology Karlsruhe Germany Institute of Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe Germany

The Large Scale data Facility (LSDF) at the Karlsruhe Institute of technology was started end of 2009 with the aim of supporting the growing requirements of data intensive experiments. In close cooperation with the involved scientific communities, the LSDF provides them not only with adequate storage space but with a directly attached analysis farm and - more importantly - with value added services for their big scientific data-sets. Analysis workflows are supported through the mixed Hadoop and Open Nebula Cloud environments directly attached to the storage, and enable the efficient processing of the experimental data. Metadata handling is a central part of the LSDF, where a metadata repository, community specific metadata schemes, graphical tools, and APIs were developed for accessing and efficiently organizing the stored data-sets.

关键词： Software data visualization Communities Microscopy Throughput Protocols Memory

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Perspective of the Large Scale data Facility (LSDF) Supporting Nuclear Fusion Applications

Perspective of the Large Scale Data Facility (LSDF) Supporti...

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Euromicro Conference on Parallel, Distributed and Network-Based processing

作者： Rainer Stotzka Volker Hartmann Thomas Jejkal Michael Sutter Jos van Wezel Marcus Hardt Ariel García Rainer Kupsch Serguei Bourov Karlsruhe Institute of Technology (KIT) Institute for Data Processing and Electronics (IPE) Karlsruhe Germany Karlsruhe Institute of Technology (KIT) Steinbuch Centre for Computing (SCC) Karlsruhe Germany

To cope with the growing requirements of data intensive scientific experiments, models and simulations the Large Scale data Facility(LSDF) at KIT aims to support many scientific disciplines. The LSDF is a distributed storage facility at Exabyte scale providing storage, archives, data bases and meta data repositories. Open interfaces and APIs support a variety of access methods to the highly available services for high throughput data applications. Tools for an easy and transparent access allow scientists to use the LSDF without bothering with the internal structures and technologies. In close cooperation with the scientific communities the LSDF provides assistance to efficiently organize data and metadata structures, and develops and deploys community specific software on the directly connected computing infrastructure.

关键词： Communities Software Protocols Standards organizations Organizations Microscopy Distributed databases

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data-intensive analysis for scientific experiments at the Large Scale data Facility

Data-intensive analysis for scientific experiments at the La...

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IEEE Symposium on Large data Analysis and Visualization (LDAV)

作者： A.O. García S. Bourov A. Hammad V. Hartmann T. Jejkal J.C. Otte S. Pfeiffer T. Schenker C. Schmidt P. Neuberger R. Stotzka J. van Wezel B. Neumair A. Streit Steinbuch Centre of Computing Karlsruhe Institute of Technology Karlsruhe Germany Institute for Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Toxicology and Genetics Karlsruhe Institute of Technology Karlsruhe Germany Institute for Applied Computer Science Karlsruhe Institute of Technology Karlsruhe Germany

关键词： data visualization Communities Software Distributed databases Protocols Three dimensional displays Throughput

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