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Nuclear cardiology image enhancement using wavelet transform analysis

Physica Medica 2014年 30卷 e30-e30页

作者： A. Poulakis S. Angelakis G. Fountos G.S. Panagiotakis I. Asteriadis I. Valais Department of Biomedical Engineering Technological Educational Institute of Athens 12210 Athens Greece Biomedical Informatics and Technology Graduate Program School of Computer Science University of Crete Department of Medical Physics Medical School University of Patras 265 00 Patras Greece Department of Nuclear Medicine General Hospital of Kavala 65 500 Kavala Greece

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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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A Data Management in a Private Cloud Storage Environment Utilizing High Performance Distributed File Systems

A Data Management in a Private Cloud Storage Environment Uti...

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IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises

作者： Tiago S. Soares M. A. R. Dantas Douglas D. J. de Macedo Michael A. Bauer Informatics and Statistic Department (INE) Federal University of Santa Catarina (UFSC) Post-Graduate Program of Knowledge Engineering and Management Federal University of Santa Catarina (UFSC) Computer Science Department (CSD) University of Western Ontario (UWO)

ISBN: (纸本)9781479904051

The new trend in the process of data-intensive management indicates the importance of a distributed file system for both Internet large scale services and cloud computing environments. I/O latency and application buffering sizes are two of a number of issues that are essential to be analysed on different class of distributed file systems. In this paper, it is presented a research work comparing four different high performance distributed file systems. Those systems were employed to support a medical image server application in a private storage environment. Experimental results highlight the importance of an appropriate distributed file system to provide a differential level of performance considering application specific characteristics.

关键词： Distributed File System DICOM HDF5 Telemedicine

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Inference of tumor phylogenies with improved somatic mutation discovery

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17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Salari, Raheleh Saleh, Syed Shayon Kashef-Haghighi, Dorna Khavari, David Newburger, Daniel E. West, Robert B. Sidow, Arend Batzoglou, Serafim Department of Computer Science Stanford University 353 Serra Mall Stanford CA 94305 United States Biomedical Informatics Training Program Stanford University 353 Serra Mall Stanford CA 94305 United States Department of Pathology Stanford University School of Medicine 300 Pasteur Drive Stanford CA 94305 United States Department of Genetics Stanford University School of Medicine 300 Pasteur Drive Stanford CA 94305 United States

ISBN: (纸本)9783642371943

Next-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technologies to detect mutations across multiple, genetically related tumors, current methods do not exploit available phylogenetic information to improve the accuracy of their variant calls. Here, we present a novel algorithm that uses somatic single nucleotide variations (SNVs) in multiple, related tissue samples as lineage markers for phylogenetic tree reconstruction. Our method then leverages the inferred phylogeny to improve the accuracy of SNV discovery. Experimental analyses demonstrate that our method achieves up to 32% improvement for somatic SNV calling of multiple related samples over the accuracy of GATK's Unified Genotyper, the state of the art multisample SNV caller. © 2013 Springer-Verlag.

关键词： Tumors

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Text and data mining for biomedical discovery 18

Text and data mining for biomedical discovery

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18th Pacific Symposium on Biocomputing, PSB 2013

作者： Gonzalez, Graciela Cohen, Kevin Bretonnel Kann, Maricel G. Greene, Casey S. Leaman, Robert Hahn, Udo Shah, Nigam Ye, Jieping Department of Biomedical Informatics Arizona State University ScottsdaleAZ85259 United States Computational Bioscience Program U. Colorado School of Medicine AuroraCO United States Department of Biological Science University of Maryland Baltimore County BaltimoreMD21250 United States Department of Genetics Geisel School of Medicine at Dartmouth HanoverNH03755 United States National Center for Biotechnology Information BethesdaMD20894 United States Friedricdh Schiller University Jena Language and Information Engineering Lab. Jena Germany Stanford Center for Biomedical Informatics Research StanfordCA94305 United States Computer Science and Engineering Arizona State University TempeAZ85287 United States

ISBN: (纸本)9781627480161

The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health related terms to integrate consumer-friendly synonyms for medical terminologies.

关键词： Data mining

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Erratum: Large meta-analysis of genome-wide association studies identifies five loci for lean body mass

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Nature communications 2017年第1期8卷 1414页

作者： M Carola Zillikens Serkalem Demissie Yi-Hsiang Hsu Laura M Yerges-Armstrong Wen-Chi Chou Lisette Stolk Gregory Livshits Linda Broer Toby Johnson Daniel L Koller Zoltán Kutalik Jian'an Luan Ida Malkin Janina S Ried Albert V Smith Gudmar Thorleifsson Liesbeth Vandenput Jing Hua Zhao Weihua Zhang Ali Aghdassi Kristina Åkesson Najaf Amin Leslie J Baier Inês Barroso David A Bennett Lars Bertram Rainer Biffar Murielle Bochud Michael Boehnke Ingrid B Borecki Aron S Buchman Liisa Byberg Harry Campbell Natalia Campos Obanda Jane A Cauley Peggy M Cawthon Henna Cederberg Zhao Chen Nam H Cho Hyung Jin Choi Melina Claussnitzer Francis Collins Steven R Cummings Philip L De Jager Ilja Demuth Rosalie A M Dhonukshe-Rutten Luda Diatchenko Gudny Eiriksdottir Anke W Enneman Mike Erdos Johan G Eriksson Joel Eriksson Karol Estrada Daniel S Evans Mary F Feitosa Mao Fu Melissa Garcia Christian Gieger Thomas Girke Nicole L Glazer Harald Grallert Jagvir Grewal Bok-Ghee Han Robert L Hanson Caroline Hayward Albert Hofman Eric P Hoffman Georg Homuth Wen-Chi Hsueh Monica J Hubal Alan Hubbard Kim M Huffman Lise B Husted Thomas Illig Erik Ingelsson Till Ittermann John-Olov Jansson Joanne M Jordan Antti Jula Magnus Karlsson Kay-Tee Khaw Tuomas O Kilpeläinen Norman Klopp Jacqueline S L Kloth Heikki A Koistinen William E Kraus Stephen Kritchevsky Teemu Kuulasmaa Johanna Kuusisto Markku Laakso Jari Lahti Thomas Lang Bente L Langdahl Lenore J Launer Jong-Young Lee Markus M Lerch Joshua R Lewis Lars Lind Cecilia Lindgren Yongmei Liu Tian Liu Youfang Liu Östen Ljunggren Mattias Lorentzon Robert N Luben William Maixner Fiona E McGuigan Carolina Medina-Gomez Thomas Meitinger Håkan Melhus Dan Mellström Simon Melov Karl Michaëlsson Braxton D Mitchell Andrew P Morris Leif Mosekilde Anne Newman Carrie M Nielson Jeffrey R O'Connell Ben A Oostra Eric S Orwoll Aarno Palotie Stephen C J Parker Munro Peacock Markus Perola Annette Peters Ozren Polasek Richard L Prince Katri Räikkönen Stuart H Ralston Samuli Ripatti John A Robbins Jerome I Rotter Igor Rudan Veikko Department of Internal Medicine Erasmus MC Rotterdam 3000 The Netherlands. Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA) Leiden 2593 The Netherlands. Department of Biostatistics Boston University School of Public Health Boston MA 02118 USA. Hebrew SeniorLife Institute for Aging Research Roslindale MA 02131 USA. Harvard Medical School Boston MA 02115 USA. Molecular and Integrative Physiological Sciences Program Harvard School of Public Health Boston MA 02115 USA. Program in Personalized and Genomic Medicine and Department of Medicine Division of Endocrinology Diabetes and Nutrition University of Maryland School of Medicine Baltimore MD 21201 USA. Broad Institute Cambridge MA 02142 USA. Sackler Faculty of Medicine Department of Anatomy and Anthropology Tel Aviv University Tel Aviv 6997801 Israel. Department of Twin Research and Genetic Epidemiology King's College London St Thomas' Campus London WC2R 2LS UK. Department of Epidemiology Erasmus MC Rotterdam 3000 The Netherlands. Department of Medical Genetics University of Lausanne Lausanne 1011 Switzerland. Swiss Institute of Bioinformatics Lausanne 1015 Switzerland. Centre Hospitalier Universitaire (CHUV) University Institute for Social and Preventive Medicine Lausanne 1010 Switzerland. Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN 46202 USA. MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge Biomedical Campus Cambridge CB2 OQQ UK. Institute of Epidemiology II Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany. Icelandic Heart Association Kopavogur 201 Iceland. Faculty of Medicine University of Iceland Reykjavik 101 Iceland. deCODE Genetics Reykjavik 101 Iceland. Department of Internal Medicine Institute of Medicine Sahlgrenska Academy University of Gothenburg Gothenburg SE-405 30 Sweden. Dep

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Open Information Extraction Based on Lexical-Syntactic Patterns

Open Information Extraction Based on Lexical-Syntactic Patte...

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Clarissa Castellã Xavier Vera Lúcia Strube de Lima Marlo Souza Graduate Program in Computer Science Pontifícia Universidade Católica do Rio Grande do Sul – PUCRS Porto Alegre Brazil Institute of Informatics Universidade Federal do Rio Grande do Sul – UFRGS Porto Alegre Brazil

Open Information Extraction (Open IE) is an unsupervised strategy to draw out relations from text without predefining these relations, regardless the domain. This paper describes a novel Open IE approach that performs unsupervised extraction of triples by applying a few lexical-syntactic patterns to POS-tagged texts. In order to validate this strategy we developed a prototype and compared its performance with two Open IE systems. The proposed approach achieved promising results, overcoming those from the state-of-the-art systems. The paper concludes with an analysis of errors and directions for future work.

关键词： Gold Encyclopedias Electronic publishing Internet Data mining Semantics

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Applying human computation mechanisms to Information Retrieval

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Proceedings of the ASIST Annual Meeting 2012年第1期49卷 1-10页

作者： Harris, Christopher G. Srinivasan, Padmini Informatics Program University of Iowa Iowa City IA 52242 United States Computer Science Department and Informatics Program University of Iowa Iowa City IA 52242 United States

Crowd sourcing and Games with a Purpose (GWAP) have each received considerable attention in recent years. These two human computation mechanisms assist with tasks that cannot be solved by computers alone. Despite this increased attention, much of this transformation has been limited to a few aspects of Information Retrieval (IR). In this paper, we examine these two mechanisms' applicability to IR. Using an IR model, we apply criteria to determine the suitability of these crowd sourcing and GWAP mechanisms to each step of the model. Our analysis illustrates that these mechanisms can apply to several of these steps with good returns.

关键词： Crowd sourcing Games with a purpose GWAP Human computation Information Retrieval

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Ancestry inference in complex admixtures via variable-length markov chain linkage models

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16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012

作者： Bercovici, Sivan Rodriguez, Jesse M. Elmore, Megan Batzoglou, Serafim Department of Computer Science Stanford University United States Biomedical Informatics Program Stanford University United States

ISBN: (纸本)9783642296260

Inferring the ancestral origin of chromosomal segments in admixed individuals is key for genetic applications, ranging from analyzing population demographics and history, to mapping disease genes. Previous methods addressed ancestry inference by using either weak models of linkage disequilibrium, or large models that make explicit use of ancestral haplotypes. In this paper we introduce ALLOY, an efficient method that incorporates generalized, but highly expressive, linkage disequilibrium models. ALLOY applies a factorial hidden Markov model to capture the parallel process producing the maternal and paternal admixed haplotypes, and models the background linkage disequilibrium in the ancestral populations via an inhomogeneous variable-length Markov chain. We test ALLOY in a broad range of scenarios ranging from recent to ancient admixtures with up to four ancestral populations. We show that ALLOY outperforms the previous state of the art, and is robust to uncertainties in model parameters. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Hidden Markov models

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Initial explorations on using CRFs for turkish named entity recognition

Initial explorations on using CRFs for turkish named entity ...

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24th International Conference on Computational Linguistics, COLING 2012

作者： Şseker, Gökhan Akin Eryǐgit, Güļsen ITU Informatics Institute Graduate Program in Computer Science Istanbul Technical University Istanbul 34469 Turkey Department of Computer Engineering Istanbul Technical University Istanbul 34469 Turkey

This paper reports the highest results (95% in MUC and 92% in CoNLL metric) in the literature for Turkish named entity recognition;more specifically for the task of detecting person, location and organization entities in general news texts. We give an in depth analysis of the previous reported results and make comparisons with them whenever possible. We use conditional random fields (CRFs) as our statistical model. The paper presents initial explorations on the usage of rich morphological structure of the Turkish language as features to CRFs together with the use of some basic and generative gazetteers. © 2012 The COLING.

关键词： Random processes

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