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2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013

作者： Yang, Shu Pham, Lisa Christadore, Lisa M. Schaus, Scott Kolaczyk, Eric D. Department of Mathematics and Statistics Boston University Boston MA United States Bioinformatics Program Boston University Boston MA United States Department of Chemistry Boston University Boston MA United States

ISBN: (纸本)9781479902484

Gene sets have been widely used on genome-scale data for various purposes. Ideally, gene sets should have multiple scales that can explain biological processes in different scales and depth, which is often missing from most popular algorithmically defined gene sets. We propose a principled way to generate multiscale gene sets based on protein-protein interaction (PPI) networks and techniques from multiscale harmonic analysis. Specifically, on a yeast PPI network, we adopt the diffusion wavelets tool developed by Coifman and Maggioni and modify it for heavy tail graphs. Then, we define gene sets through a tiling of the PPI network based on the scaling functions. We compare our multiscale gene sets to two standard gene set databases (GO and KEGG) and gene sets derived from a hierarchical clustering method. We find that our gene sets have a large, non-trivial overlap with the standard databases, and yet still have a sizeable non-overlap as well. In addition, the sense of scale from our gene sets also matches well with that from GO. Finally, we use yeast cell cycle experiments to demonstrate the potential usage of our multiscale gene sets. © 2013 IEEE.

关键词： Proteins

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Broadening the scope of nanopublications

Broadening the scope of nanopublications

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10th International Conference on The Semantic Web: Semantics and Big Data, ESWC 2013

作者： Kuhn, Tobias Barbano, Paolo Emilio Nagy, Mate Levente Krauthammer, Michael Department of Pathology Yale University New Haven United States Department of Sociology Particular of Modeling and Simulation ETH Zurich Switzerland Department of Mathematics Yale University United States Program for Computational Biology and Bioinformatics Yale University United States

ISBN: (纸本)9783642382871

In this paper, we present an approach for extending the existing concept of nanopublications-tiny entities of scientific results in RDF representation-to broaden their application range. The proposed extension uses English sentences to represent informal and underspecified scientific claims. These sentences follow a syntactic and semantic scheme that we call AIDA (Atomic, Independent, Declarative, Absolute), which provides a uniform and succinct representation of scientific assertions. Such AIDA nanopublications are compatible with the existing nanopublication concept and enjoy most of its advantages such as information sharing, interlinking of scientific findings, and detailed attribution, while being more flexible and applicable to a much wider range of scientific results. We show that users are able to create AIDA sentences for given scientific results quickly and at high quality, and that it is feasible to automatically extract and interlink AIDA nanopublications from existing unstructured data sources. To demonstrate our approach, a web-based interface is introduced, which also exemplifies the use of nanopublications for non-scientific content, including meta-nanopublications that describe other nanopublications. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Semantics

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Multiscale gene sets from protein interaction networks

Multiscale gene sets from protein interaction networks

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Shu Yang Lisa Pham Lisa M. Christadore Scott Schaus Eric D. Kolaczyk Department of Mathematics and Statistics Boston University Boston MA USA Bioinformatics Program Boston University Boston MA USA Boston University Boston MA US Department of Chemistry Boston University Boston MA USA

关键词： Proteins Standards Genomics bioinformatics Biological system modeling Computational modeling

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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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Recovering the tree-like trend of evolution despite extensive lateral genetic transfer: A probabilistic analysis

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

作者： Roch, Sebastien Snir, Sagi Department of Mathematics Bioinformatics Program UCLA United States Institute of Evolution University of Haifa Haifa 31905 Israel

ISBN: (纸本)9783642296260

Lateral gene transfer (LGT) is a common mechanism of non-vertical evolution where genetic material is transferred between two more or less distantly related organisms. It is particularly common in bacteria where it contributes to adaptive evolution with important medical implications. In evolutionary studies, LGT has been shown to create widespread discordance between gene trees as genomes become mosaics of gene histories. In particular, the Tree of Life has been questioned as an appropriate representation of bacterial evolutionary history. Nevertheless a common hypothesis is that prokaryotic evolution is primarily tree-like, but that the underlying trend is obscured by LGT. Extensive empirical work has sought to extract a common tree-like signal from conflicting gene trees. Here we give a probabilistic perspective on the problem of recovering the tree-like trend despite LGT. Under a model of randomly distributed LGT, we show that the species phylogeny can be reconstructed even in the presence of surprisingly many (almost linear number of) LGT events per gene tree. Our results, which are optimal up to logarithmic factors, are based on the analysis of a robust, computationally efficient reconstruction method and provides insight into the design of such methods. Finally we show that our results have implications for the discovery of highways of gene sharing. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Genes

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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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Secondary structure predictions for long RNA sequences based on inversion excursions - Preliminary results 12

Secondary structure predictions for long RNA sequences based...

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2012 ACM Conference on bioinformatics, Computational Biology and Biomedicine, BCB 2012

作者： Yehdego, Daniel T. Zhang, Boyu Taufer, Michela Kodimala, Vikram Kumar Reddy Vegesna, Rahulsimham Viswakula, Sameera Johnson, Kyle L. Leung, Ming-Ying Computational Science Program University of Texas El Paso United States Department of Computer and Information Sciences University of Delaware United States Bioinformatics Program Border Biomedical Research Center University of Texas El Paso United States Department of Mathematical Sciences University of Texas El Paso United States Department of Biological Sciences University of Texas El Paso United States Department of Mathematics and Statistics Old Dominion University Norfolk VA United States University of Texas M. D. Anderson Cancer Center Houston TX United States

ISBN: (纸本)9781450316705

The tremendous demand on computer memory and computing time for prediction of complex secondary structures limits the applicability of most RNA secondary structure prediction programs available to short RNA sequences. We propose to approach this problem by segmenting a long RNA sequence into shorter non-overlapping chunks, predicting the secondary structures of each chunk individually, and then assembling the prediction results to give the structure of the original sequence. The selection of cutting points is a crucial component of the approach. Noting that stem-loops and pseudoknots always contain an inversion, we developed two cutting methods, the centered and optimized methods, for segmenting long RNA sequences based on inversion excursions. For the majority of the sequences in a dataset of 50 RNAs from the RFAM database, the prediction algorithm PKnotsRG used with these cutting methods produces more accurate secondary structures than those predicted for the whole sequence without segmentation. Both the centered and optimized cutting methods outperform the naïve regular segmentation. These results support our claim that cutting is a promising approach for the prediction of long RNA sequences, and choosing the cutting points intelligently by considering sequence features such as inversion excursions can further enhance prediction accuracy.

关键词： Forecasting

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Copy number alteration signature defines undifferentiated pleomorphic sarcomas and leiomyosarcomas with poor prognosis

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BMC Proceedings 2013年第2期7卷 1-1页

作者： Sara Martoreli Silveira Rolando Rolando Andre Rios Villacis Mateus de Camargo Barros Filho Sandra Drigo Linde Silvia Regina Rogatto Fabio Albuquerque Marchi Cristovam Scapulatempo Neto Isabela Werneck da Cunha Ademar Lopes Neogene Laboratory CIPE A. C. Camargo Hospital São Paulo Brazil Department of Urology Faculty of Medicine UNESP Botucatu São Paulo Brazil Institute of Mathematics and Statistics Inter-Institutional Program on Bioinformatics São Paulo Brazil Department of Pathology Barretos Cancer Hospital (Pio XII Foundation) Barretos Brazil Department of Anatomic Pathology A. C. Camargo Hospital São Paulo Brazil Department of Pelvic Surgery A.C. Camargo Cancer Hospital São Paulo Brazil

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Empirical normalization for quadratic discriminant analysis and classifying cancer subtypes

Empirical normalization for quadratic discriminant analysis ...

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10th International Conference on Machine Learning and Applications, ICMLA 2011

作者： Kon, Mark A. Nikolaev, Nikolay Dept. of Mathematics and Statistics Program in Bioinformatics Boston University Boston MA 02215 United States Dept. of Mathematics and Statistics Boston University Boston MA 02215 United States

ISBN: (纸本)9780769546070

We introduce a new discriminant analysis method (Empirical Discriminant Analysis or EDA) for binary classification in machine learning. Given a dataset of feature vectors, this method defines an empirical feature map transforming the training and test data into new data with components having Gaussian empirical distributions. This map is an empirical version of the Gaussian copula used in probability and mathematical finance. The purpose is to form a feature mapped dataset as close as possible to Gaussian, after which standard quadratic discriminants can be used for classification. We discuss this method in general, and apply it to some datasets in computational biology. © 2011 IEEE.

关键词： Discriminant analysis

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Proceedings of the 3rd IPLeiria's International Health Congress Abstracts

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BMC HEALTH SERVICES RESEARCH 2016年第3期16卷 111-242页

作者： [Anonymous] Health Sciences Research Unit: Nursing Collegue of College of Health Technology of Coimbra Coimbra Portugal Sisters Hospitallers of the Sacred Heart of Jesus Casa de Saúde Rainha Santa Isabel Coimbra Portugal Research Unit for Sport and Physical Activity Faculty of Sport Sciences and Physical Education University of Coimbra Coimbra Portugal Complementary Sciences- INESCC Coimbra Portugal CUF Hospitals Lisbon Portugal Breast Unit Champalimaud Clinical Center Lisbon Portugal Swiss Centre for Medical Simulation & Swiss Association of Simulation in Healthcare Basel Switzerland Basel University Hospital Basel Switzerland HELPO Association Cascais Portugal Centro de Linguística Universidade de Lisboa Lisboa Portugal Centro Superior de Estudios Universitarios La Salle Madrid Spain Universidade da Madeira Funchal Portugal Serviço de Saúde da Região Autónoma da Madeira E.P.E. Funchal Portugal Escola Superior de Enfermagem de Lisboa Lisboa Portugal Instituto Superior Miguel Torga Coimbra Portugal Centro de Investigação do Núcleo de Estudos e Intervenção Cognitivo Comportamental Universidade de Coimbra Coimbra Portugal Escola Superior de Saúde de Leiria & Unidade de Investigação em Saúde Instituto Politécnico de Leiria Leiria Portugal National School of Public Health & Centro de Investigação em Saúde Pública Universidade Nova de Lisboa Lisboa Portugal Agrarian Superior School Polytechnic Institute of Viseu Viseu Portugal Laboratory of Microbiology Department of Biological Sciences Faculty of Pharmacy University of Porto Porto Portugal Unit for Multidisciplinary Biomedical Research Institute of Biomedical Sciences Abel Salazar University of Porto Porto Portugal Agrupamento de Centros de Saúde (ACES) Baixo Mondego Figueira da Foz Portugal Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal Centro de Salud La Paz Badajoz España Centro de Salud San Fernando Badajoz España Complejo Hospitalario Universitario de Badajoz Badajoz España Hospital Don Beni

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