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Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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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Diagnosis of breast cancer by tissue analysis

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Chinese Journal of Cancer Research 2013年第1期25卷 39-45页

作者： Debnath Bhattacharyya Samir Kumar Bandyopadhyay Tai-hoon Kim Computer Science and Engineering Department NFET NSHM Knowledge Campus-Durgapur Department of Computer Science and Engineering University of Calcutta School of Computing and Information Systems University of Tasmania Australia

In this paper, we propose a technique to locate abnormal growth of cells in breast tissue and suggest further pathological test, when require. We compare normal breast tissue with malignant invasive breast tissue by a series of image processing steps. Normal ductal epithelial cells and ductal/lobular invasive carcinogenic cells also consider for comparison here in this paper. In fact, features of cancerous breast tissue （invasive） are extracted and analyses with normal breast tissue. We also suggest the breast cancer recognition technique through image processing and prevention by controlling p53 gene mutation to some extent.

关键词： Mammography drug administration edge detection epithelium

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Top-k entities query processing on uncertainly fused multi-sensory data

Top-k entities query processing on uncertainly fused multi-s...

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作者： Liu, Dexi Wan, Changxuan Xiong, Naixue Park, Jong Hyuk Rho, Seungmin Jiangxi Key Laboratory of Data and Knowledge Engineering School of Information Technology Jiangxi University of Finance and Economics Nanchang China Department of Computer Science Georgia State University Atlanta GA United States Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of School of Electrical Engineering Korea University Seoul Korea Republic of

Sensor fusion is the combining of sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The natural uncertainty exists in these data because sensors are not precise enough. Hence, the intuitive method to store this kind of data is using uncertain database. Finding the top-k entities according to one or more attributes is a powerful technique when the uncertain database contains large quantity of data. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. We propose a method to process entity-based global top-k aggregate queries in uncertain database, which returns the top-k entities that have the highest aggregate value. Our method has two levels, entity state generation and G-topk-E query processing. In the former level, entity states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming-based global top-k entity query processing is employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. © 2012 Springer-Verlag London Limited.

关键词： Query processing

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SocioPhone: Everyday face-to-face interaction monitoring platform using multi-phone sensor fusion 13

SocioPhone: Everyday face-to-face interaction monitoring pla...

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11th Annual International Conference on Mobile systems, Applications, and Services, MobiSys 2013

作者： Lee, Youngki Min, Chulhong Hwang, Chanyou Lee, Jaeung Hwang, Inseok Ju, Younghyun Yoo, Chungkuk Moon, Miri Lee, Uichin Song, Junehwa School of Information Systems Singapore Management University Singapore Computer Science Department KAIST Korea Republic of Web Science and Technology Division KAIST Korea Republic of Center for Mobile Software Platform KAIST Korea Republic of Knowledge Service Engineering Department KAIST Korea Republic of

ISBN: (纸本)9781450316729

In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turnmonitoring technique is highly accurate and energy-efficient under diverse real-life situations. Copyright 2013 ACM.

关键词： Linguistics

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An hidden markov model-based transcription factor mining method

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Journal of Bionanoscience 2013年第1期7卷 66-71页

作者： Wu, Chunguo Li, Gaoyang Wu, Xiaozhou Han, Xiaosong Wang, Binghong Breis, Jesualdo Tomás Fernández Wang, Fei Liang, Yanchun Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China Business School University of Shanghai for Science and Technology Shanghai 200093 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433 China Department of Informatics and Systems University of Murcia 30100 Murcia Spain

A text mining algorithm named HMM-TFM (Hidden Markov Model based transcription factor name mining) is presented. The proposed algorithm does not need a dictionary of transcription factor names. A small verb set is defined to filter sentences. Transcription factor names are mined according to the part of speech tagged by hidden Markov model. Experimental results show that the recall and precision of HMM-TFM achieve 74.2% and 77.9%, respectively. Copyright © 2013 American Scientific Publishers.

关键词： data mining

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Demo-SocioPhone: Everyday face-to-face interaction monitoring platform using multi-phone sensor fusion

Demo-SocioPhone: Everyday face-to-face interaction monitorin...

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11th Annual International Conference on Mobile systems, Applications, and Services, MobiSys 2013

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Development of tourism information systems

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2013年第3期133卷 562-566页

作者： Matsuo, Tokuro Samejima, Masaki Hashimoto, Kiyota Advanced Institute of Industrial Technology 1-10-40 Higashi-Oi Shinagawa-ku Tokyo 140-0011 Japan Department of Multimedia Engineering Graduate School of Information Science and Technology Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan College of Knowledge and Information Systems School Sustainable System Sciences Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai Osaka 599-8531 Japan

This article describes innovations in tourism information systems and their changing after the Internet technologies have appeared. Airline companies in United States started to provide computer reservation system to be made air ticket reservation and to be confirmed their seats in the aircraft. These systems were provided to travel agency to help their business. After around 2000, as the Internet develops, the tourism information systems have been becoming an increasingly development for B2C. Recent research contributions are including innovative user support;user preferences on the web can be analyzed by data mining technology;and the system make a travel plan based on users' preferences. © 2013 The Institute of Electrical Engineers of Japan.

关键词： data mining

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Erratum to: Methods for evaluating medical tests and biomarkers

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Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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Online identification of complex multi-input-multi-output system based on generic evolving neuro-fuzzy inference system

Online identification of complex multi-input-multi-output sy...

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IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Mahardhika Pratama Sreenatha.G. Anavatti Matthew Garratt Edwin Lughofer School of Engineering and Information Technology University of New South Wales Canberra Australia Department of Knowledge-Based Mathematical Systems Johannes Kepler University of Linz Linz Austria

Nowadays, unmanned aerial vehicles (UAV) play a noteworthy role in miscellaneous defence and civilian operation. A major facet in the UAV control system is an identification phase feeding the valid and up-to-date information of the system dynamic in order to generate proper adaptive control action to handle various UAV maneuvers. UAV, however, constitutes a complex system possessing a highly non-linear property. Conversely, the learning environment in modeling UAV's dynamic varies overtime and demands online learning scheme encouraging a fully adaptive and evolving algorithm with a mild computational load to settle the task. In contrast, contemporaneous literatures scrutinizing the identification of UAV dynamic yet rely on offline or batched learning procedures. Evolving neuro-fuzzy system (ENFS) where the landmarks are flexible rule base and usable in the time-critical applications offers a promising impetus in the UAV research field, and in particular its identification standpoint. The principle cornerstone is ENFS can commence its learning mechanism from scratch with an empty rule base and very limited expert knowledge. Nonetheless, it can perform automatic knowledge building from streaming data without catastrophic forgetting previous valid knowledge which is alike autonomous mental development of human brain. This paper elaborates the identification of rotary wing UAV based on our incipient ENFS algorithm termed generic evolving neuro-fuzzy system (GENEFIS). In summary, our algorithm can not only trace footprint of the UAV dynamic but also ameliorate the performance of existing ENFS in terms of predictive quality and resultant rule base burden.

关键词： Decision support systems Conferences Adaptive systems Intelligent systems Manganese Erbium

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Evolving fuzzy rule-based classifier based on GENEFIS

Evolving fuzzy rule-based classifier based on GENEFIS

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Mahardhika Pratama Sreenatha G. Anavatti Edwin Lughofer School of Engineering and Information Technology University of New South Wales Canberra ACT Australia Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Linz Austria

ISBN: (纸本)9781479900213

This paper presents a novel evolving fuzzy rule-based classifier stemming from our recently developed algorithm for regression problem termed generic evolving neuro-fuzzy system (GENEFIS). On the one hand, the novel classifier namely GENEFIS-class is composed of two different architectures specifically zero and first orders which are dependent on the type of consequent used. On the other hand, GENEFIS-class refurbishes GENEFIS algorithm as the main learning engine to conform classification requirement. The interesting property of GENEFIS is its fully flexible rule base and its computationally efficient algorithm. GENEFIS can initiate its learning process from scratch with an empty rule base and highly narrow expert knowledge. The fuzzy rules are then flourished based on the novelty of streaming data via their statistical contribution. Conversely, the fuzzy rules, which contribute little during their lifespan, can be pruned by virtue of their contributions up to the end of training process. Meanwhile, the fuzzy rules and fuzzy sets, which are redundant, can be merged to purpose a transparent rule base. Online feature selection process coupled during the training process can be undertaken to cope with possible combinatorial rule explosion drawback. All of these are fruitful to grant significant reduction of rule base load while retaining the classification accuracy which is in line with online real-time necessity. The efficacy of GENEFIS-class was numerically validated exploiting real world and synthetic problems and compared with state-of-the-art algorithms where it generally speaking outperforms other algorithms in terms of classification performance and rule-base complexity.

关键词： Training Classification algorithms Covariance matrices Fuzzy systems Computer architecture Mathematical model Redundancy

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