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Methods for Evaluating Medical Tests and Biomarkers

Diagnostic and Prognostic Research 2017年第1期1卷 1-34页

作者： Gowri Gopalakrishna Miranda Langendam Patrick Bossuyt Mariska Leeflang Rob Scholten Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Marie Westwood Jos Kleinen Richard Riley Gary Collins Hans Reitsma Karel Moons Antonia Zapf Katharina Kramer Annika Hoyer Oliver Kuss J. Ensor R. D. Riley J. J. Deeks E. C. Martin Gerta Rücker Martin Schumacher Susanne Steinhauser Joie Ensor Kym Snell Brian Willis Jon Deeks Thomas Debray 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 Sabine Van Huffel Jan Verbakel 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 Peter Selby Doug Altman William Rosenberg Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Johannes Reitsma Anne Rutjes Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Bavo De Cock Daan Nieboer Ewout W. Steyerberg Micael J. Pencina 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 Zoe Plummer Barnaby Reeves Chris Rogers Andrew Mumford Kurtis Lee Veerle Verheyden Gianni D. Angelini Gavin J. Murphy Jeremy Huddy Melody Ni George Hanna Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands Department of Clinical Epidemiology and Biostatistics Academic Medical Center University of Amsterdam Amsterdam Netherlands Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK EPPI-Centre Department of Social Science University College London London UK Division of Health and Social Care Research King’s College London London UK College of Computer and Information Science Northeastern University Boston USA University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK Institute of Applied Health Research University of Birmingham Birmingham UK School of Social and Community Medicine University of Bristol Bristol UK Kleijnen Systematic Reviews Ltd York UK Research Institute for Primary Care and Health Sciences Keele University Keele UK Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Cochrane Netherlands University Medical Center Utrecht Utrecht The Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Institute for Biometry and Epidemiology German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf Germany Manchester Pharmacy School University of Manchester Manchester UK Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center – University of Freiburg Stefan-Meier-Str. 26 79104 Freiburg Germany

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Sentiment classification in Persian: Introducing a mutual information-based method for feature selection

Sentiment classification in Persian: Introducing a mutual in...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Ayoub Bagheri Mohamad Saraee Franciska de Jong Intelligent Database Data Mining and Bioinformatics Laboratory Electrical and Computer Engineering Department Isfahan University of Technology Isfahan Iran School of Computing Science and Engineering University of Salford Manchester UK Human Media Interaction University of Twente Enschede Netherlands

With the enormous growth of online reviews in Internet, sentiment analysis has received more and more attention in information retrieval and natural language processing community. Up to now there are very few researches conducted on sentiment analysis for Persian documents. This paper considers the problem of sentiment classification for online customer reviews in Persian language. One of the challenges of Persian language is using of a wide variety of declensional suffixes. Another common problem of Persian text is word spacing. In Persian in addition to white space as interwords space, an intra-word space called pseudo-space separates word's part. One more noticeable challenge in customer reviews in Persian language is that of utilizing many informal or colloquial words in text. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on a lemmatization approach for Persian language and is employed Naive Bayes learning algorithm for classification. Additionally we present a new feature selection method based on the mutual information method to extract the best feature collection from the initial extracted features. Finally we evaluate the performance of the model on a manually gathered collection of cellphone reviews, where the results show the effectiveness of the proposed model.

关键词： Feature extraction Mathematical model Classification algorithms Equations Mutual information Training data mining

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Erratum to: Making sense of big data in health research: towards an EU action plan

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Genome medicine 2016年第1期8卷 118页

作者： Charles Auffray Rudi Balling Inês Barroso László Bencze Mikael Benson Jay Bergeron Enrique Bernal-Delgado Niklas Blomberg Christoph Bock Ana Conesa Susanna Del Signore Christophe Delogne Peter Devilee Alberto Di Meglio Marinus Eijkemans Paul Flicek Norbert Graf Vera Grimm Henk-Jan Guchelaar Yi-Ke Guo Ivo Glynne Gut Allan Hanbury Shahid Hanif Ralf-Dieter Hilgers Ángel Honrado D Rod Hose Jeanine Houwing-Duistermaat Tim Hubbard Sophie Helen Janacek Haralampos Karanikas Tim Kievits Manfred Kohler Andreas Kremer Jerry Lanfear Thomas Lengauer Edith Maes Theo Meert Werner Müller Dörthe Nickel Peter Oledzki Bertrand Pedersen Milan Petkovic Konstantinos Pliakos Magnus Rattray Josep Redón I Màs Reinhard Schneider Thierry Sengstag Xavier Serra-Picamal Wouter Spek Lea A I Vaas Okker van Batenburg Marc Vandelaer Peter Varnai Pablo Villoslada Juan Antonio Vizcaíno John Peter Mary Wubbe Gianluigi Zanetti European Institute for Systems Biology and Medicine 1 avenue Claude Vellefaux 75010 Paris France. cauffray@***. CIRI-UMR5308 CNRS-ENS-INSERM-UCBL Université de Lyon 50 avenue Tony Garnier 69007 Lyon France. cauffray@***. Luxembourg Centre for Systems Biomedicine University of Luxembourg 7 Avenue des Hauts Fourneaux 4362 Esch-sur-Alzette Luxembourg. rudi.balling@uni.lu. Wellcome Trust Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK. Health Services Management Training Centre Faculty of Health and Public Services Semmelweis University Kútvölgyi út 2 1125 Budapest Hungary. Centre for Personalised Medicine Linköping University 581 85 Linköping Sweden. Translational & Bioinformatics Pfizer Inc. 300 Technology Square Cambridge MA 02139 USA. Institute for Health Sciences IACS - IIS Aragon San Juan Bosco 13 50009 Zaragoza Spain. ELIXIR Wellcome Genome Campus Hinxton Cambridge CB10 1SD UK. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Lazarettgasse 14 AKH BT25.2 1090 Vienna Austria. Department of Laboratory Medicine Medical University of Vienna Lazarettgasse 14 AKH BT25.2 1090 Vienna Austria. Max Planck Institute for Informatics Campus E1 4 66123 Saarbrücken Germany. Príncipe Felipe Research Center C/Eduardo Primo Yúfera 3 46012 Valencia Spain. University of Florida Institute of Food and Agricultural Sciences (IFAS) 2033 Mowry Road Gainesville FL 32610 USA. Bluecompanion Ltd 6 London Street (second floor) London W2 1HR UK. Technology Data & Analytics KPMG Luxembourg Société Coopérative 39 Avenue John F. Kennedy 1855 Luxembourg Luxembourg. Department of Human Genetics Department of Pathology Leiden University Medical Centre Einthovenweg 20 2333 ZC Leiden The Netherlands. Information Technology Department European Organization for Nuclear Research (CERN) 385 Route de Meyrin 1211 Geneva 23 Switzerland. Julius Center for Health Sciences and Primary Care University Medical Center

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Challenges in DNA sequence analysis on a production grid 2

Challenges in DNA sequence analysis on a production grid

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2012 EGI Community Forum / EMI 2nd Technical Conference, EGICF-EMITC 2012

作者： Van Schaik, Barbera D.C. Santcroos, Mark Korkhov, Vladimir Jongejan, Aldo Willemsen, Marcel Van Kampen, Antoine H.C. Olabarriaga, S lvia D. Bioinformatics Laboratory Department of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center Amsterdam Netherlands Faculty of Applied Math and Control Processes St. Petersburg State University Russia Biosystems Data Analysis Swammerdam Institute for Life Science University of Amsterdam Amsterdam Netherlands

Modern DNA sequencing machines produce data in the range of 1-100 GB per experiment and with ongoing technological developments this amount is rapidly increasing. The majority of experiments involve re-sequencing of human genomes and exomes to find genomic regions that are associated with disease. There are many sequence analysis tools freely available, e.g. for sequence alignment, quality control and variant detection, and frequently new tools are developed to address new biological questions. Since 2008 we use workflow technology to allow easy incorporation of such software in our data analysis pipelines, as well as to leverage grid infrastructures for the analysis of large datasets in parallel. The size of the datasets has grown from 1 GB to 70 GB in 3 years, therefore adjustments were needed to optimize these workflows. Procedures have been implemented for faster data transfer to and from grid resources, and for fault recovery at run time. A split-and-merge procedure for a frequently used sequence alignment tool, BWA, resulted in a three-fold reduction of the total time needed to complete an experiment and increased efficiency by a reduction in number of failures. The success rate was increased from 10% to 70%. In addition, steps to resubmit workflows for partly failed workflows were automated, which saved user intervention. Here we present our current procedure of analyzing data from DNA sequencing experiments, comment on the experiences and focus on the improvements needed to scale up the analysis of genomics data at our hospital. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence.

关键词： data transfer

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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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On the integration of graph exploration and data analysis: The creative exploration toolkit

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2012年 7250卷 301-312页

作者： Haun, Stefan Gossen, Tatiana Nürnberger, Andreas Kötter, Tobias Thiel, Kilian Berthold, Michael R. Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany Department for Bioinformatics and Information Mining University of Konstanz Germany

ISBN: (纸本)9783642318290

To enable discovery in large, heterogenious information networks a tool is needed that allows exploration in changing graph structures and integrates advanced graph mining methods in an interactive visualization framework. We present the Creative Exploration Toolkit (CET), which consists of a state-of-the-art user interface for graph visualization designed towards explorative tasks and support tools for integration and communication with external data sources and mining tools, especially the data-mining platform KNIME. All parts of the interface can be customized to fit the requirements of special tasks, including the use of node type dependent icons, highlighting of nodes and clusters. Through an evaluation we have shown the applicability of CET for structure-based analysis tasks. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： User interfaces

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Legal constraints on genetic data processing in European grids

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10th HealthGrid Conference and the 4th International Workshop on science Gateways for Life sciences, IWSG-Life 2012

作者： Mouw, Evert Van't Noordende, Guido Van Kampen, Antoine H.C. Louter, Baas Santcroos, Mark Olabarriaga, Silvia D. Department of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Department of Medical Informatics Academic Medical Center University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Biosystems Data Analysis Swammerdam Institute for Life Science University of Amsterdam Netherlands

ISBN: (纸本)9781614990536

European laws on privacy and data security are not explicit about the storage and processing of genetic data. Especially whole-genome data is identifying and contains a lot of personal information. Is processing of such data allowed in computing grids? To find out, we looked at legal precedents in related fields, current literature, and interviews with legal experts. We found that processing of genetic data is only allowed on distributed systems with specific security measures, both technical and organizational. Informed consent, although important, offers no substitute for such requirements. © 2012 The authors and IOS Press. All rights reserved.

关键词： Digital storage

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Integrated support for neuroscience research: From study design to publication

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10th HealthGrid Conference and the 4th International Workshop on science Gateways for Life sciences, IWSG-Life 2012

作者： Shahand, Shayan Caan, Matthan W.A. Van Kampen, Antoine H.C. Olabarriaga, Sílvia D. Department of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam PO Box 22700 1100 DE Amsterdam Netherlands Dept. of Radiology Brain Imaging Center University of Amsterdam Netherlands Biosystems Data Analysis Swammerdam Institute for Life Science University of Amsterdam Netherlands

ISBN: (纸本)9781614990536

Computational neuroscience is a new field of research in which neurodegenerative diseases are studied with the aid of new imaging techniques and computation facilities. Researchers with different expertise collaborate in these studies. A study requires scalable computational and storage capacity and information management facilities to succeed. Many virtual laboratories are proposed and developed to facilitate these studies, however most of them cover only the parts related to the computational data processing. In this paper we describe and analyse the phases of the computational neuroscience studies including the actors, the tasks they perform, and the characteristics of each phase. Based on these we identify the required properties and functionalities of a virtual laboratory that supports the actors and their tasks throughout the complete study. © 2012 The authors and IOS Press. All rights reserved.

关键词： Neurodegenerative diseases

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Predicting bone metastasis of breast cancer using context-specific miRNA regulation modules

Predicting bone metastasis of breast cancer using context-sp...

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第五届全国生物信息学与系统生物学学术大会

作者： Xionghui Zhou Fengji Liang Juan Liu Yinghui Li Shanguang Chen Jianghui Xiong Bioinformatics Group and Data Coordination Center State Key Lab of Space Medicine Fundamentals and Application (SMFA) China Astronaut Research and Training Center (ACC) Beijing China Bioinformatics Group and Data Coordination Center State Key Lab of Space Medicine Fundamentals and Application (SMFA) China Astronaut Research and Training Center (ACC) Beijing China School of Computer Science Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

Background: The bone is the most frequent target of metastatic breast ***, the underlying molecular mechanisms are *** many microarray studies have reported gene signatures that can predict metastasis in breast cancer, predicting cancer metastasis to a specific organ, such as to bone, remains a challenge in cancer ***: Previously we proposed a concept CoMi (Context Specific miRNA activity), which could quantify the regulation activity of miRNA on specific context (CoMi activity) by calculating the statistical difference of the expression level between the miRNAtargets and non-targets in a given context (GO term biological process), and demonstrated its application on predicting *** we extend this concept to CoMi module and use it to predict whether the cancer will metastasize to ***, CoMi features which are significant associated with the bone metastasis are identified by Cox proportional hazards *** on the significant CoMi features, miRNAs regulate on the same context are regarded as a significant miRNA regulation *** each significant CoMi module is used to predict bone metastasis of breast cancer patients by sum up all the risk scores (Cox proportional hazards regression) of CoMi features in the ***: We tested the proposed method on a breast cancer cohort contain 855 samples' gene expression profiles together with bone metastasis *** bone metastasis specific CoMi modules are identified, such as module 'hemopoiesis', which has a log rank p value of 9.05e-07 in train dataset, 0.0075 in test dataset and 0.0031 in independent test *** the hematopoietic stem cell niche is a potential therapeutic target for bone metastatic tumors, our results might facilitate to identify the miRNAs regulation modules involved in hematopoietic stem cell ***: We proposed the concept of CoMi activity module, and identified some CoMi activity modules which

关键词： Context specific miRNA activity metastasis organ prediction miRNA regulation network

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Predicting bone metastasis of breast cancer using context-specific miRNA regulation modules

Predicting bone metastasis of breast cancer using context-sp...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Xionghui Zhou Fengji Liang Juan Liu Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Bioinformatics Group and Data Coordination Center State Key Lab of Space Medicine Fundamentals and Application(SMFA)China Astronaut Research and Training Center(ACC) School of Computer Science Wuhan University.WuhanChina

Background:The bone is the most frequent target of metastatic breast ***, the underlying molecular mechanisms are *** many microarray studies have reported gene signatures that can predict metastasis in breast cancer,predicting cancer metastasis to a specific organ,such as to bone,remains a challenge in cancer study. Methods:Previously we proposed a concept CoMi(Context Specific miRNA activity), which could quantify the regulation activity of miRNA on specific context(CoMi activity) by calculating the statistical difference of the expression level between the miRNA targets and non-targets in a given context(GO term biological process),and demonstrated its application on predicting *** we extend this concept to CoMi module and use it to predict whether the cancer will metastasize to ***,CoMi features which are significant associated with the bone metastasis are identified by Cox proportional hazards regression. Based on the significant CoMi features,miRNAs regulate on the same context are regarded as a significant miRNA regulation *** each significant CoMi module is used to predict bone metastasis of breast cancer patients by sum up all the risk scores(Cox proportional hazards regression) of CoMi features in the module. Results:We tested the proposed method on a breast cancer cohort contain 855 samples' gene expression profiles together with bone metastasis *** bone metastasis specific CoMi modules are identified,such as module 'hemopoiesis',which has a log rank p value of 9.05e-07 in train dataset,0.0075 in test dataset and 0.0031 in independent test dataset. Considering the hematopoietic stem cell niche is a potential therapeutic target for bone metastatic tumors,our results might facilitate to identify the miRNAs regulation modules involved in hematopoietic stem cell niche. Conclusions:We proposed the concept of CoMi activity module,and identified some CoMi activity modules which could predict bone metas

关键词： Context specific miRNA activity metastasis organ prediction miRNA regulation network

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