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Information visualization services in a library? A public health case study

Bulletin of the Association for Information Science and Technology 2009年第5期35卷

作者： Barrie Hayes Hong Yi Andrés Villaveces Bioinformatics librarian and Collaboration Center manager in the Biomedical Informatics Research Support and Training Program of the Health Sciences Library at the University of North Carolina at Chapel Hill Sernior research software developer Renaissance Computing Institute University of North Carolina at Chapel Hill Research assistant professor Department of Epidemiology and the Injury Prevention Research Center at the University of North Carolina at Chapel Hill

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On Self-Organizing Feature Map (SOFM) Formation by Direct Optimization Through a Genetic Algorithm

On Self-Organizing Feature Map (SOFM) Formation by Direct Op...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： José Everardo B. Maia Guilherme A. Barreto André L.V. Coelho Department of Teleinformatics Engineering Federal University of Ceará Fortaleza Ceara Brazil Department of Statistics and Computing State Univ. of Ceara Fortaleza Ceara Brazil Department of Statistics and Computing State University of Ceará Fortaleza Ceara Brazil Department of Teleinformatics Engineering Federal Univ. of Ceara Fortaleza Ceara Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza Ceara Brazil

This paper examines the formation of self-organizing feature maps (SOFM) by the direct optimization of a cost function through a genetic algorithm (GA). The resulting SOFM is expected to produce simultaneously a topologically correct mapping between input and output spaces and a low quantization error. The proposed approach adopts a cost (fitness) function which is a weighted combination of indices that measure these two aspects of the map quality, specifically, the quantization error and the Pearson correlation coefficient between the corresponding distances in input and output spaces. The resulting maps are compared with those generated by the Kohonen's self-organizing map (SOM) algorithm in terms of the quantization error (QE), the weighted topological error (WTE) and the Pearson correlation coefficient (PCC) indices. The experiments show the proposed approach produces better values of the quality indices as well as is more robust to outliers.

关键词： Gallium Neurons Arrays Genetic algorithms Indexes Optimization Genetics

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AlzPharm: Integration of neurodegeneration data using RDF

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BMC Bioinformatics 2007年第SUPPL. 3期8卷 S4-S4页

作者： Lam, Hugo Y.K. Marenco, Luis Clark, Tim Gao, Yong Kinoshita, June Shepherd, Gordon Miller, Perry Wu, Elizabeth Wong, Gwendolyn T. Liu, Nian Crasto, Chiquito Morse, Thomas Stephens, Susie Cheung, Kei-Hoi Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Anesthesiology Yale University New Haven CT United States Department of Neurobiology Yale University New Haven CT United States Department of Molecular Cellular and Developmental Biology Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Initiative in Innovative Computing Harvard University Cambridge MA United States Massachusetts General Hospital Boston MA United States Alzheimer Research Forum Burlington MA United States Oracle Burlington MA United States

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results su

关键词： Data integration

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The generation challenge programme platform: Semantic standards and workbench for crop science

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International Journal of Plant Genomics 2008年第unknown期2008卷 369601页

作者： Bruskiewich, Richard Senger, Martin Davenport, Guy Ruiz, Manuel Rouard, Mathieu Hazekamp, Tom Takeya, Masaru Doi, Koji Satoh, Kouji Costa, Marcos Simon, Reinhard Balaji, Jayashree Akintunde, Akinnola Mauleon, Ramil Wanchana, Samart Shah, Trushar Anacleto, Mylah Portugal, Arllet Ulat, Victor Jun Thongjuea, Supat Braak, Kyle Ritter, Sebastian Dereeper, Alexis Skofic, Milko Rojas, Edwin Martins, Natalia Pappas, Georgios Alamban, Ryan Almodiel, Roque Barboza, Lord Hendrix Detras, Jeffrey Manansala, Kevin Mendoza, Michael Jonathan Morales, Jeffrey Peralta, Barry Valerio, Rowena Zhang, Yi Gregorio, Sergio Hermocilla, Joseph Echavez, Michael Yap, Jan Michael Farmer, Andrew Schiltz, Gary Lee, Jennifer Casstevens, Terry Jaiswal, Pankaj Meintjes, Ayton Wilkinson, Mark Good, Benjamin Wagner, James Morris, Jane Marshall, David Collins, Anthony Kikuchi, Shoshi Metz, Thomas McLaren, Graham Van Hintum, Theo Crop Research Informatics Laboratory International Rice Research Institute (IRRI) DAPO Box 7777 Metro Manila Philippines Crop Research Informatics Laboratory International Maize and Wheat Improvement Center (CIMMYT) DF 06600 Mexico Apdo. Postal 6-641 Mexico Centre International de Recherche Agronomique Pour le Développement (CIRAD) 34398 Montpellier Cedex 5 Avenue Agropolis France Bioversity International 00057 Maccarese (Fiumicino) Rome Via dei Tre Denari 472/a Italy National Institute for Agrobiological Sciences (NIAS) Tsukuba Ibaraki 305-8602 Kannondai 2-1-2 Japan Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) Brasilia DF 70770-900 Parque Estaçao Biologia Final W5 Norte Brazil Centro Internacional de la Papa (CIP) Lima 12 Avenida La Molina 1895 La Molina Peru International Crops Research Institute for the Semi-Arid Tropics Patancheru Andhra Pradesh 502324 India International Center for Agricultural Research in the Dry Areas Aleppo P.O. Box 5466 Syrian Arab Republic National Center for Genetic Engineering and Biotechnology 113 Thailand Science Park Klong Luang Pathumthani 12120 Phahonyothin Road Klong 1 Thailand Institute of Computer Science College of Arts and Sciences University of the Philippines Los Banos Laguna 4031 Philippines Department of Computer Science University of the Philippines Melchor Hall Diliman Quezon City 1101 Philippines National Center for Genome Resources Santa Fe NM 87505 2935 Rodeo Park Drive East United States Scottish Crop Research Institute Invergowrie Dundee DD2 5DA United Kingdom Department of Plant Breeding Cornell University Ithaca NY 14853 United States African Centre for Gene Technologies Lynnwood Ridge 0040 P.O. Box 75011 South Africa Department of Medical Genetics Faculty of Medicine University of British Columbia Vancouver BC V6T 1Z3 Canada School of Computing Science Simon Fraser Universtiy Burnaby BC V5A 1S6 8888 University Drive Canada Bioinformatics Graduate Program Genome Sciences Centre BC

The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform;(ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data;(iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.

关键词： GENOMICS GERMPLASM resources PLANT breeding PLANT genetics COMPUTERS in biology BIOinformatics -- Software

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Optimizing wireless communication using adaptive packet sizing and turbo codes

Optimizing wireless communication using adaptive packet sizi...

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2006 Wireless Telecommunications Symposium, WTS

作者： Mittal, Manish S. Gemmill, Jill Orthner, Helmuth F. Department of Electrical and Computer Engineering University of Alabama Birmingham United States Department of Academic Computing University of Alabama Birmingham United States Dept. of HSA Health Informatics Program University of Alabama Birmingham United States

ISBN: (纸本)1424400465

Unlike wired networks, wireless networks have very dynamic channel properties and the data communication suffers from interference, multi-path fading and noise. Since wireless devices are mobile, factors such as energy consumption, effective coverage area and most of all reliability and quality of service are of key importance. The 802.11a/b/g wireless protocols employ static link layer control and transmit packets/frames with pre-determined sizes. This paper focuses on benefits derived from combining adaptive packet/frame sizing with Forward Error Correcting (FEC) Turbo codes. Protecting the data frames on the channel, using FEC enhances efficiency of dynamic packet sizing and is shown to increase energy efficiency and communication range. Adaptive packet sizing software from SmartPackets Inc. and Turbo code simulations are used to discover parameters that are common to both and affect communication efficiency. A multi-fold improvement in throughput is found when these technologies are used simultaneously. © 2006 IEEE.

关键词： Turbo codes

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Optimizing Wireless Communication using Adaptive Packet Sizing and Turbo Codes

Optimizing Wireless Communication using Adaptive Packet Sizi...

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Wireless Telecommunications Symposium, WTS

作者： Manish S. Mittal Jill Gemmill Helmuth F. Orthner Department of Electrical and Computer Engineering University of Alabama Birmingham UK Department of Academic Computing University of Alabama Birmingham UK Department of HSA-Health Informatics Program University of Alabama Birmingham UK

Unlike wired networks, wireless networks have very dynamic channel properties and the data communication suffers from interference, multi-path fading and noise. Since wireless devices are mobile, factors such as energy consumption, effective coverage area and most of all reliability and quality of service are of key importance. The 802.11a/b/g wireless protocols employ static link layer control and transmit packets/frames with pre-determined sizes. This paper focuses on benefits derived from combining adaptive packet/frame sizing with forward error correcting (FEC) turbo codes. Protecting the data frames on the channel, using FEC enhances efficiency of dynamic packet sizing and is shown to increase energy efficiency and communication range. Adaptive packet sizing software from SmartPackets Inc. and turbo code simulations are used to discover parameters that are common to both and affect communication efficiency. A multi-fold improvement in throughput is found when these technologies are used simultaneously

关键词： Wireless communication Turbo codes Forward error correction Wireless networks Data communication Interference Fading Energy consumption Telecommunication network reliability Quality of service

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Handling Data Sparseness in Gene Network Reconstruction

Handling Data Sparseness in Gene Network Reconstruction

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： G.B. Bezerra T.V. Barra F.J. von Zuben L.N. de Castro Laboratory of Bioinformatics and Bio-Inspired Computing (LBiC) Department of Computer Engineering and Industrial Automation State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program on Informatics Catholic University of Santos Sao Paulo Brazil

One of the main problems related to regulatory network reconstruction from expression data concerns the small size and low quality of the available dataset. When trying to infer a model from little information it is necessary to give much more precedence to generalization, rather than specificity, otherwise, any attempt will be fated to overfitting. In this paper we address this issue by focusing on data sparseness and noisy information, and propose a density estimation technique that achieves regularized curves when data is scarce. We first compare the proposed method with the EM algorithm for mixture models on density estimation problems. Next, we apply the method, together with Bayesian networks, on realistic simulations of static gene networks, and compare the obtained results with the standard discrete Bayesian network model. We intend to demonstrate that adopting a discrete approach is not justifiable when only a small amount of information is available.

关键词： Intelligent networks Bayesian methods Genetics Bioinformatics Computer networks Reverse engineering Cellular networks Noise robustness Laboratories Data engineering

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An Exponential Matrix Product Based Representation for Generalized Hypergeometric Functions of TypepFp

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Applied Numerical Analysis & Computational Mathematics 2005年第1期2卷

作者： N. A. Baykara İrem Yaman Metin Demiralp Mathematics Department Faculty of Sciences and Letters Marmara University Göztepe 34722 İstanbul Turkey Group for Science and Methods of Computing Computational Science and Engineering Program Informatics Institute İstanbul Technical University Maslak 34469 İstanbul Turkey Phone: +90 212 285 70 77 Fax: +90 212 285 70 73 Phone: +90 212 285 70 82 Fax: +90 212 285 70 73

In this work, a new representation is developed for generalized hypergeometric functions of type p F p . To this end a first order vector differential equation is constructed in a way such that the derivative of the unknown vector has unit matrix coefficient. The solution vector differential equation is rewritten in the form of a series expansion. An infinite process of factor extractions and annihilations is employed to construct finally a vector differential equation that can be easily solved. Truncation of this scheme can be used to get approximations to hypergeometric functions of type p F p . A simple, yet meaningful, implementation seems to give quite promising results. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

关键词： Exponential matrix factorization generalized hypergemetric function

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Appointments for 2005-2006 term

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2005年第7期13卷 773-782页

作者： Ranganathan, Nagarajan Agrawal, Vishwani D. Chakradhar, Srimat T. Chakrabarty, Krishnendu Courtois, Bernard DeMara, Ronald Hu, Xiaobo Sharon Ismail, Yehea I. Jha, Niraj K. John, Lizy Kurian Ker, Ming-Dou Koren, Israel Liu, Bin-Da Marculescu, Diana Marculescu, Radu Narayanan, Vijaykrishnan Nassif, Sani R. Nowick, Steven M. Sapatnekar, Sachin S. Sherlekar, Sunil Sylvester, Dennis Vemuri, Ranga Pham, Michael Department of Computer Science and Engineering University of South Florida Tampa FL 33620 United States Department of Computer Science and Engineering Nanomaterials and Nanomanufacturing Research Center University of South Florida Tampa United States IEEE United States Systems United States Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ United States IETE India ACM NEC Laboratories America Inc. Princeton NJ United States Duke University Durham NC United States ACM SIGDA Sigma Xi United States Laboratory of Techniques of Informatics and Microelectronics for Computer Architectecture ASME IMAPS IEEE Computer Society University of Central Florida Orlando United States Computer Engineering Program SEECS Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Northwestern University Evanston IL United States Department of Electrical Engineering Princeton University Princeton NJ United States Association for Computing Machinery Austin United States UT Austin Engineering Foundation United States Department of Electronics Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering University of Massachusetts Amherst United States Department of Electrical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Electrical and Computer Engineering Faculty Carnegie Mellon University Pittsburgh PA United States Computer Science and Engineering Department Pennsylvania State University University Park United States IBM Austin Research Laboratory Austin TX United States Columbia University New York NY United States Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States Tata Consultancy Services Centre of Excellence for Embedded Systems India Department of Electrical Engineering IIT Bombay India Department of Electrical Engineering Uni

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An intrusion detection system using ideas from the immune system

An intrusion detection system using ideas from the immune sy...

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Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004

作者： De Paula, Fabrício Sérgio De Castro, Leandro Nunes De Geus, Paulo Lício Computing Institute State University of Campinas Avenida Albert Einstein 1251 CEP: 13084-971 Campinas/SP Brazil Graduation Program on Informatics Catholic University of Santos Rua Dr. Carvalho de Mendonca 144 CEP: 11070-906 Santos/SP Brazil

ISBN: (纸本)0780385152

This paper proposes an intrusion detection frame-work and presents a prototype for an intrusion detection system based on it. This framework takes architectural inspiration from the human immune system and brings desirable features to intrusion detection systems, such as automated intrusion recovery, attack signature extraction, and potential to improve behavior-based detection. These features are enabled through intrusion evidence detection. The prototype, called ADENOIDS, is designed to deal with application attacks, extracting signature for remote buffer overflow attacks. The framework and ADENOIDS are described and experimental results are presented.

关键词： Computer science

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