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15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015

作者： Cao, Xi Hang Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Department of Computer and Information Sciences College of Science and Technology Temple University 1925 N. 12th Street PhiladelphiaPA United States

ISBN: (纸本)9781467379830

Gene expression data are widely used in classification tasks for medical diagnosis. data scaling is recommended and helpful for learning the classification models. In this study, we propose a data scaling algorithm to transform the data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative density function of the data. The proposed algorithm is robust to outliers, and experimental results show that models learned using data scaled by the proposed algorithm generally outperform the ones using min-max mapping and z-score which are currently the most commonly used data scaling algorithms. © 2015 IEEE.

关键词： Gene expression

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A Robust data Scaling Algorithm for Gene Expression Classification

A Robust Data Scaling Algorithm for Gene Expression Classifi...

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IEEE International Conference on Bioinformatics and Bioengineering

作者： Xi Hang Cao Zoran Obradovic Center for Data Analytics and Biomedical Informatics Department of Computer and Information Sciences College of Science and Technology Temple University

ISBN: (纸本)9781467379847

关键词： Logistics data models Approximation algorithms Robustness Density functional theory Approximation methods Random variables

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Spatial Prior for Nonparametric Road Scene Parsing

Spatial Prior for Nonparametric Road Scene Parsing

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International IEEE Conference on Intelligent Transportation Systems

作者： Shuai Di Honggang Zhang Xue Mei Danil Prokhorov Haibin Ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Toyota Research Institute North America Ann Arbor USA Center for Data Analytics and Biomedical Informatics Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (纸本)9781467365970

Parsing road scene images taken from vehicle mounted cameras provides important information for high level tasks in automated on-road vehicles. In this paper we adopt the nonparametric framework for this problem and present a simple yet effective strategy to integrate spatial prior into the framework. Unlike natural scene images, road scene images in our problem typically have very stable scene layout, which motivates us to explore such layout for improving scene labeling. In particular, the spatial distribution of each semantic category is obtained from a set of previously observed data. Then, such distributions, in the form of histograms, are integrated into the nonparametric labeling framework to guide scene parsing. Compared with previous approaches, our solution is very efficient in both computation and memory usage, since there is no complicated semantic training involved. For evaluation, we collected three video datasets on three different trips and ran the proposed algorithm on all of them, both within each trip or cross trip. The experimental results show advantages of our algorithm.

关键词： high level tasks nonparametric framework histograms histograms Parsing Road layouts rise level vehicle Frameworks

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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Hospital pricing estimation by Gaussian conditional random fields based regression on graphs

Hospital pricing estimation by Gaussian conditional random f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： A. Polychronopoulou Z. Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA

Accurate estimation of what a day in a hospital costs and what the hospital charges is of high interest to many parties, including health care providers, medical insurance companies, health researchers, and uninsured patients. The problem is complex, as the cost-to-charge ratio varies greatly from hospital to hospital and over time. In addition, the cost-to-charge ratio is often not reported, and in such cases group average values from similar hospitals are used. In this study we address the problem of estimating the cost-to-charge ratio at the hospital level by utilizing structured regression on a temporal graph of more than 4,000 hospitals, observed over 8 years, constructed from the National Inpatient Sample database. In the proposed approach, the cost-to-charge estimates at individual hospitals for a certain month obtained by an artificial neural network were used as unstructured components in the Gaussian Conditional Random Fields (GCRF) model. The diagnosis codes of treatments in each hospital were used to create a similarity metric that represents correlation among hospital specializations. The estimates of cost-to-charge ratio obtained using convex optimization of the GCRF parameters on the constructed graph were much better than those relying on group average based cost-to-charge estimates. In addition, cost-to-charge ratio estimates by our GCRF model outperformed regression by nonlinear artificial neural networks.

关键词： Hospitals databases Accuracy Predictive models Measurement Estimation

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Author Correction: Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function

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Nature communications 2019年第1期10卷 2068页

作者： Gail Davies Max Lam Sarah E Harris Joey W Trampush Michelle Luciano W David Hill Saskia P Hagenaars Stuart J Ritchie Riccardo E Marioni Chloe Fawns-Ritchie David C M Liewald Judith A Okely Ari V Ahola-Olli Catriona L K Barnes Lars Bertram Joshua C Bis Katherine E Burdick Andrea Christoforou Pamela DeRosse Srdjan Djurovic Thomas Espeseth Stella Giakoumaki Sudheer Giddaluru Daniel E Gustavson Caroline Hayward Edith Hofer M Arfan Ikram Robert Karlsson Emma Knowles Jari Lahti Markus Leber Shuo Li Karen A Mather Ingrid Melle Derek Morris Christopher Oldmeadow Teemu Palviainen Antony Payton Raha Pazoki Katja Petrovic Chandra A Reynolds Muralidharan Sargurupremraj Markus Scholz Jennifer A Smith Albert V Smith Natalie Terzikhan Anbupalam Thalamuthu Stella Trompet Sven J van der Lee Erin B Ware B Gwen Windham Margaret J Wright Jingyun Yang Jin Yu David Ames Najaf Amin Philippe Amouyel Ole A Andreassen Nicola J Armstrong Amelia A Assareh John R Attia Deborah Attix Dimitrios Avramopoulos David A Bennett Anne C Böhmer Patricia A Boyle Henry Brodaty Harry Campbell Tyrone D Cannon Elizabeth T Cirulli Eliza Congdon Emily Drabant Conley Janie Corley Simon R Cox Anders M Dale Abbas Dehghan Danielle Dick Dwight Dickinson Johan G Eriksson Evangelos Evangelou Jessica D Faul Ian Ford Nelson A Freimer He Gao Ina Giegling Nathan A Gillespie Scott D Gordon Rebecca F Gottesman Michael E Griswold Vilmundur Gudnason Tamara B Harris Annette M Hartmann Alex Hatzimanolis Gerardo Heiss Elizabeth G Holliday Peter K Joshi Mika Kähönen Sharon L R Kardia Ida Karlsson Luca Kleineidam David S Knopman Nicole A Kochan Bettina Konte John B Kwok Stephanie Le Hellard Teresa Lee Terho Lehtimäki Shu-Chen Li Christina M Lill Tian Liu Marisa Koini Edythe London Will T Longstreth Oscar L Lopez Anu Loukola Tobias Luck Astri J Lundervold Anders Lundquist Leo-Pekka Lyytikäinen Nicholas G Martin Grant W Montgomery Alison D Murray Anna C Need Raymond Noordam Lars Nyberg William Ollier Goran Papenberg Alison Pattie Ozren Polasek Russell A Poldrack Bruce M Psaty Sim Centre for Cognitive Ageing and Cognitive Epidemiology Department of Psychology School of Philosophy Psychology and Language Sciences The University of Edinburgh Edinburgh EH8 9JZ UK. Institute of Mental Health Singapore 539747 Singapore. Medical Genetics Section Centre for Genomic & Experimental Medicine MRC Institute of Genetics & Molecular Medicine University of Edinburgh Western General Hospital Edinburgh EH4 2XU UK. BrainWorkup LLC Los Angeles 90033 CA USA. Department of Psychiatry and Behavioral Sciences Keck School of Medicine University of Southern California Los Angeles 90033 CA USA. Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology & Neuroscience King's College London De Crespigny Park Denmark Hill London SE5 8AF UK. Research Centre of Applied and Preventive Cardiovascular Medicine University of Turku Turku 20520 Finland. Department of Internal Medicine Satakunta Central Hospital Pori 28100 Finland. Centre for Global Health Research Usher Institute of Population Health Sciences and Informatics University of Edinburgh Teviot Place Edinburgh EH8 9AG Scotland. Max Planck Institute for Molecular Genetics Berlin 14195 Germany. Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle 98101 Washington USA. Department of Psychiatry Icahn School of Medicine at Mount Sinai New York 10029 NY USA. Mental Illness Research Education and Clinical Center (VISN 3) James J. Peters VA Medical Center Bronx 10468 NY USA. Department of Psychiatry Brigham and Women's Hospital Harvard Medical School Boston 02115 MA USA. NORMENT K.G. Jebsen Centre for Psychosis Research Department of Clinical Science University of Bergen Bergen 5021 Norway. Dr. Einar Martens Research Group for Biological Psychiatry Center for Medical Genetics and Molecular Medicine Haukeland University Hospital Bergen 5020 Norway. Center for Psychiatric Neuroscience Feinstein Institute for Medical Resea

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions...

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Analyzing anatomical structures of branching topology through elastic matching of tree encodings

Analyzing anatomical structures of branching topology throug...

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IEEE International Symposium on biomedical Imaging

作者： Angeliki Skoura Vasileios Megalooikonomou Computer Engineering and Informatics Department University of Patras Patras Greece Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA

Automated techniques for the analysis of anatomical tree-shape structures provide insight into the association between topology and underlying pathology. In this paper we present a framework to analyze tree topologies using representative encodings of parent-child node relationships and sequence matching techniques. Our methodology introduces the notion of elastic matching between tree encodings which results in revealing corresponding groups of nodes between the compared trees. Experimental evaluation using a dataset of breast ductal trees regarding radiological findings of breast cancer demonstrates the effectiveness of the proposed method which outperforms state-of-the-art tree characterization techniques in terms of sensitivity, specificity and accuracy.

关键词： Vegetation Topology Encoding Breast biomedical imaging Labeling Sensitivity

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How disordered is my protein and what is its disorder for? A guide through the "dark side" of the protein universe

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Intrinsically disordered proteins 2016年第1期4卷 e1259708页

作者： Philippe Lieutaud François Ferron Alexey V Uversky Lukasz Kurgan Vladimir N Uversky Sonia Longhi Aix-Marseille Université AFMB UMR Marseille France CNRS AFMB UMR Marseille France. Center for Data Analytics and Biomedical Informatics Department of Computer and Information Sciences College of Science and Technology Temple University Philadelphia PA USA. Department of Computer Science Virginia Commonwealth University Richmond VA USA. Department of Molecular Medicine and USF Health Byrd Alzheimer Research Institute Morsani College of Medicine University of South Florida Tampa FL USA Laboratory of Structural Dynamics Stability and Folding of Proteins Institute of Cytology Russian Academy of Sciences St. Petersburg Russia.

In the last 2 decades it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins lack a stable 3D structure, are ubiquitous and fulfill essential biological functions. Their conformational heterogeneity is encoded in their amino acid sequences, thereby allowing intrinsically disordered proteins or regions to be recognized based on properties of these sequences. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to structural determination with X-ray crystallization. This article discusses a comprehensive selection of databases and methods currently employed to disseminate experimental and putative annotations of disorder, predict disorder and identify regions involved in induced folding. It also provides a set of detailed instructions that should be followed to perform computational analysis of disorder.

关键词： disorder databases and metaservers induced folding intrinsic disorder intrinsically disordered proteins intrinsically disordered regions prediction methods

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Methods for Evaluating Medical Tests and Biomarkers

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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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