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IEEE International Conference on E-Science Workshops

作者： Yassene Mohammed Shayan Shahand Vladimir Korkhov Angela C.M. Luyf Barbera D.C. van Schaik Matthan W.A. Caan Antoine H.C. van Kampen Magnus Palmblad Silvia D. Olabarriaga Biomolecular Mass Spectrometry Unit Department of Parasitology Leiden University Medical Center Netherlands Regional Computing Center of Lower Saxony and L3S University of Hannover Germany Bioinformatics Laboratory Department of Clinical Epidemiology Biostatistics and Bioinformatics AMC University of Amsterdam Netherlands Faculty of Applied Math and Control Processes Saint Petersburg State University Russia Department of Radiology AMC University of Amsterdam Netherlands Biosystems Data Analysis Swammerdam Institute for Life Sciences University of Amsterdam Amsterdam Netherlands

As the focus of e-Science is moving toward the forth paradigm and data intensive science, data access remains dependent on the architecture of the used e-Science infrastructure. Such architecture is in general job-driven, i.e., a (grid) job is a sequence of commands that run on the same worker node. Making use of the infrastructure involves having a parallelized application. This is done foremost by data decomposition. In general practice of parallel programming, data decomposition depends on the programmer's experience and knowledge about the used data and the algorithm/application. On the other hand, data mining scientists have an established foundation for data decomposition, automatic decomposition methods are already in use, methodologies and patterns are defined. Our experience in porting biomedical applications to the Dutch e-Science infrastructure shows that the used data decomposition to gain parallelism fit to some degree a subgroup of the data mining decomposition patterns, i.e., object set decomposition. In this paper we discuss porting three biomedical packages to a grid computing environment, two for medical imaging and one for DNA sequencing. We show how the data access of the applications was reengineered around the executables to make use of the parallel capacity of e-Science infrastructure.

关键词： Diffusion tensor imaging Educational institutions data mining Parallel processing Biomedical imaging Pipelines

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Organizing our knowledge of biodiversity

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Bulletin of the Association for Information Science and Technology 2011年第4期37卷

作者： Hilmar Lapp Robert A. Morris Terry Catapano Donald Hobern Norman Morrison Assistant director of informatics National Evolutionary Synthesis Center (NESCent) Professor emeritus of computer science University of Massachusetts/Boston and biodiversity informatics staff Harvard University Herbaria librarian Columbia University Libraries and vice president Plazi Director Atlas of Living Australia and former TDWG chair Ontologies and data standards manager Natural Environment Research Council Environmental Bioinformatics Centre (NEBC) & The University of Manchester United Kingdom

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Meeting report: "Metagenomics, metadata and meta-analysis" (M3) workshop at the pacific symposium on biocomputing 2010

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Standards in Genomic Sciences 2010年第3期2卷 357-360页

作者： Hirschman, Lynette Sterk, Peter Field, Dawn Wooley, John Cochrane, Guy Gilbert, Jack Kolker, Eugene Kyrpides, Nikos Meyer, Folker Mizrachi, Ilene Nakamura, Yasukazu Sansone, Susanna-Assunta Schriml, Lynn Tatusova, Tatiana White, Owen Yilmaz, Pelin Information Technology Center The MITRE Corporation Bedford MA United States NERC Center for Ecology and Hydrology Oxford OX1 3SR United Kingdom Wellcome Trust Sanger Institute Hinxton Cambridge United Kingdom University of California San Diego La Jolla CA United States European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Hinxton Cambridge United Kingdom Plymouth Marine Laboratory (PML) Plymouth United Kingdom Seattle Children's Hospital Seattle WA United States Genome Biology Program DOE Joint Genome Institute Walnut Creek CA United States Argonne National Laboratory Argonne IL 60439 9700 South Cass Avenue United States National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD United States DNA Data Bank of Japan National Institute of Genetics Yata Mishima Japan University of Maryland School of Medicine Baltimore MD United States Microbial Genomics Group Max Planck Institute for Marine Microbiology and Jacobs University Bremen Bremen Germany

This report summarizes the M3 Workshop held at the January 2010 Pacific Symposium on Biocomputing. The workshop, organized by Genomic Standards Consortium members, in-cluded five contributed talks, a series of short presentations from stakeholders in the genom-ics standards community, a poster session, and, in the evening, an open discussion session to review current projects and examine future directions for the GSC and its stakeholders.

关键词： Special Interest Group Human Microbiome Project Metadata Standard Genomic Standard Consortium International Nucleotide Sequence database Collaboration

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Machine learning identification of zeolite framework types

Machine learning identification of zeolite framework types

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2009 International Conference on Artificial Intelligence, ICAI 2009

作者： Yang, Shujiang Lach-Hab, Mohammed Vaisman, Iosif I. Blaisten-Barojas, Estela Computational Materials Science Center George Mason University Fairfax VA United States Department of Bioinformatics and Computational Biology George Mason University Manassas VA United States Department of Computational and Data Sciences George Mason University Manassas VA United States

ISBN: (纸本)9781601321091

The characteristic framework types of zeolite crystals are routinely determined by calculating coordination sequences and vertex symbols of the 3D crystal structures. This method has limitations and tends to fail when the synthesized crystals are not close to perfect and present some types of crystallographic disorder. A machine learning based Zeolite-Structure-Predictor (ZSP) model is developed to predict framework types for both near perfect and moderately disordered zeolite crystals. The ZSP uses various attributes, including topological descriptors based on a computational geometry approach and relevant physical, chemical properties of the crystals. Trained with 41 framework types, the ZSP can correctly classify zeolite crystals with over 98% accuracy. Additionally, it is shown that the ZSP model is able to predict the framework types for strongly disordered zeolite crystals with reliable success rate.

关键词： Crystal structure

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A supervised ICA algorithm for fetal ECG extraction

A supervised ICA algorithm for fetal ECG extraction

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Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP)

作者： Jenho Tsao Chao-Yang Hsu Men-Tzung Lo Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Research Center for Adaptive Data Analysis National Central University

A new semi-blind ICA algorithm, named supervised ICA, is proposed for improving the performance of ICA in FECG extraction. A supervision signal is created for assisting the usual ICA algorithm to find the desired FECG unambiguously. It is different from other semi-blind ICA algorithms in that the a priori information about the temporal structure of the desired signal is encoded in the supervision signal, instead of the object function. Performances of the new algorithm are demonstrated by real data processing. High quality FECG can be extracted from the multi-channel abdominal signals.

关键词： Independent component analysis Electrocardiography Abdomen data mining Electrodes Vectors Chaos Biomedical electronics bioinformatics data analysis

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Machine learning approach for classification of zeolite crystals

Machine learning approach for classification of zeolite crys...

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2008 International Conference on data Mining, DMIN 2008

作者： Yang, Shujiang Lach-Hab, Mohammed Vaisman, Iosif I. Blaisten-Barojas, Estela Computational Materials Science Center George Mason University MSN 6A2 Fairfax VA 22030 United States Department of Computational Biology and Bioinformatics George Mason University MSN 5B3 Manassas VA 20110 United States Department of Computational and Data Sciences George Mason University MSN 6A2 Fairfax VA 22030 United States

ISBN: (纸本)1601320620

A machine learning approach is applied to classify zeolite crystals according to their framework type. The Zeolite-Structure-Predictor is introduced based on the Random Forest algorithm. Zeolites structural data from the Inorganic Crystal Structure database (ICSD) are used to train the model. The ZSP uses sixteen attributes including topological descriptors obtained with statistical geometry and physical and chemical properties of individual zeolites. Trained with 40 framework types containing at least 5 instances per class, the ZSP can correctly classify zeolites with over 95% accuracy. The performance is shown to improve when more zeolite instances per class are available.

关键词： data mining

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Much room for improvement in deposition rates of expression microarray datasets (vol 5, pg 991, 2008)

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NATURE METHODS 2008年第12期5卷 991-991页

作者： Ochsner, Scott A. Steffen, David L. Stoeckert, Christian J., Jr. McKenna, Neil J. Department of Molecular and Cellular Biology Baylor College of Medicine One Baylor Plaza Houston Texas 77030 USA. Bioinformatics Research Center Baylor College of Medicine One Baylor Plaza Houston Texas 77030 USA. Microarray and Gene Expression Data Society and Department of Genetics University of Pennsylvania 418 Guardian Drive Philadelphia Pennsylvania 19104 USA. Department of Molecular and Cellular Biology Baylor College of Medicine One Baylor Plaza Houston Texas 77030 USA.

A letter to the editor is presented in response to an editorial in the March 2008 issue, containing the findings of a survey of papers from 20 journals from 2007.

关键词： LETTERS to the editor DNA microarrays

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High-throughput computation of pairwise sequence similarities for multiple genome comparisons using ScalaBLAST

High-throughput computation of pairwise sequence similaritie...

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IEEE/NLM Life Science Systems and Applications Workshop

作者： Anuj R. Shah Victor M. Markowitz Christopher S. Oehmen Computational Biology and Bioinformatics Pacific Northwest National Laboratory Richland WA USA Biological Data Management and Technology Center Berkeley CA USA

Genome sequence comparisons of exponentially growing data sets form the foundation for the comparative analysis tools provided by community biological data resources such as the integrated microbial genome (IMG) system at the joint genome institute (JGI). For a genome sequencing center to provide multiple-genome comparison capabilities, it must keep pace with exponentially growing collection of sequence data, both from its own genomes, and from public genomes. We present an example of how ScalaBLAST, a high-throughput sequence analysis program, harnesses increasingly critical high-performance computing to perform sequence analysis, enabling, for example, all vs. all BLAST runs across 2 million protein sequences within a day using thousands of processors as opposed to conventional comparison methods that would take years to complete.

关键词： Genomics bioinformatics Sequences Proteins Performance analysis data analysis Biology computing Computational biology Resource management Technology management

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Machine learning in the life sciences

Machine learning in the life sciences

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作者： Cios, Krzysztof J. Kurgan, Lukasz A. Reformat, Marek University of Colorado Denver Health Sciences Center United States University of Colorado Bioenergetics Institute United States Data Mining and Bioinformatics Laboratory Turkey Polish Academy of Arts and Sciences Poland Department of Electrical and Computer Engineering University of Alberta Edmonton Alta. Canada IEEE ACM ISCB

No abstract available

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Quality assessment of Affymetrix GeneChip data using the EM algorithm and a naïve Bayes classifier

Quality assessment of Affymetrix GeneChip data using the EM ...

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Howard, Brian E. Sick, Beate Perera, Imara Yang, Ju Im Winter-Sederoff, Heike Heber, Steffen Bioinformatics Research Center North Carolina State University Raleigh NC United States Institute of Data Analysis and Process Design Zurich University of Applied Science Winterthur Winterthur Switzerland Department of Plant Biology North Carolina State University Raleigh NC United States Department of Computer Science North Carolina State University Raleigh NC United States

ISBN: (纸本)1424415098

Recent research has demonstrated the utility of using supervised classification systems for automatic identification of low quality microarray data. However, this approach requires annotation of a large training set by a qualified expert. In this paper we demonstrate the utility of an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and naïve Bayes classification. On our test set, this system exhibits performance comparable to that of an analogous supervised learner constructed from the same training data. ©2007 IEEE.

关键词： Maximum principle

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