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FAIR Principles:Interpretations and Implementation Considerations

Data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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Multinomial logistic model for coinfection diagnosis between arbovirus and malaria in Kedougou

arXiv

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arXiv 2018年

作者： Loum, Mor Absa Poursat, Marie-Anne Sow, Abdourahmane Sall, Amadou Alpha Loucoubar, Cheikh Gassiat, Elisabeth Laboratoire de Mathématiques d'Orsay Univ. Paris-Sud CNRS Université Paris-Saclay Orsay91405 France Institut Pasteur de Dakar Arboviruses and Viral Hemorrhagic Fevers Unit Institut Pasteur de Dakar Biostatistics Bioinformatics and Modeling Group

In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The cocirculation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Thus, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, long duration of illness and age are mostly indicative of arboviral disease while high body temperature and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease. Copyright © 2018, The Authors. All rights reserved.

关键词： Viruses

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Correction: Quorum sensing provides a molecular mechanism for evolution to tune and maintain investment in cooperation

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The ISME journal 2021年第7期15卷 2171页

作者： Eric L Bruger Daniel J Snyder Vaughn S Cooper Christopher M Waters Department of Biological Sciences University of Idaho Moscow ID USA. Institute for Modeling Collaboration and Innovation University of Idaho Moscow ID USA. Institute for Bioinformatics and Evolutionary Studies University of Idaho Moscow ID USA. The BEACON Center for the Study of Evolution in Action Michigan State University East Lansing MI USA. Department of Microbiology and Molecular Genetics University of Pittsburgh Pittsburgh PA USA. The BEACON Center for the Study of Evolution in Action Michigan State University East Lansing MI USA. watersc3@msu.edu. Department of Microbiology and Molecular Genetics Michigan State University East Lansing MI USA. watersc3@msu.edu.

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On the use of Networks in Biomedicine

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Procedia Computer Science 2017年 110卷 498-503页

作者： Eugenio Vocaturo Pierangelo Veltri Department of Computer Science Modeling Electronic and Systems Engineering (DIMES) University of Calabria Italy Bioinformatics Laboratory Surgical and Medical Science Department University Magna Graecia of Catanzaro Italy

The concept of “neural network” emerges by electronic models inspired to the neural structure of human brain. Neural networks aim to solve problems currently out of computer's calculation capacity, trying to mimic the role of human brain. Recently, the number of biological based applications using neural networks is growing up. Biological networks represent correlations, extracted from sets of clinical data, diseases, mutations, and patients, and many other types of clinical or biological features. Biological networks are used to model both the state of a range of functionalities in a particular moment, and the space-time distribution of biological and clinical events. The study of biological networks, their analysis and modeling are important tasks in life sciences. Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. Starting from preliminary notions about neural networks, we focus on biological networks and discuss some well-known applications, like protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network, phylogenetic trees and special networks. Finally, we consider the use of biological network inside a proposed model to map health related data.

关键词： Biological Networks Protein-Protein Interaction Networks (PPIn) Gene Regulatory Networks (GRN) Metabolic Networks Signaling Networks Neuronal Networks Food Webs Phylogenetic Trees Special Networks and Hierarchies Health Care Model

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PLUMED Tutorials: a collaborative, community-driven learning ecosystem

arXiv

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arXiv 2024年

作者： Tribello, Gareth A. Bonomi, Massimiliano Bussi, Giovanni Camilloni, Carlo Armstrong, Blake I. Arsiccio, Andrea Aureli, Simone Ballabio, Federico Bernetti, Mattia Bonati, Luigi Brookes, Samuel G.H. Faidon Brotzakis, Z. Capelli, Riccardo Ceriotti, Michele Chan, Kam-Tung Cossio, Pilar Dasetty, Siva Donadio, Davide Ensing, Bernd Ferguson, Andrew L. Fraux, Guillaume Gale, Julian D. Gervasio, Francesco Luigi Giorgino, Toni Herringer, Nicholas S.M. Hocky, Glen M. Hoff, Samuel E. Invernizzi, Michele Languin-Cattoën, Olivier Leone, Vanessa Limongelli, Vittorio Lopez-Acevedo, Olga Marinelli, Fabrizio Martinez, Pedro Febrer Masetti, Matteo Mehdi, Shams Michaelides, Angelos Murtada, Mhd Hussein Parrinello, Michele Piaggi, Pablo M. Pietropaolo, Adriana Pietrucci, Fabio Pipolo, Silvio Pritchard, Claire Raiteri, Paolo Raniolo, Stefano Rapetti, Daniele Rizzi, Valerio Rydzewski, Jakub Salvalaglio, Matteo Schran, Christoph Seal, Aniruddha Zadeh, Armin Shayesteh Silva, Tomás F.D. Spiwok, Vojtěch Stirnemann, Guillaume Sucerquia, Daniel Tiwary, Pratyush Valsson, Omar Vendruscolo, Michele Voth, Gregory A. White, Andrew D. Wu, Jiangbo Centre for Quantum Materials and Technology School of Mathematics and Physics Queen’s University Belfast United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Via Bonomea 265 Trieste34136 Italy Department of Biosciences University of Milano Milano Italy School of Molecular and Life Sciences Curtin University GPO Box U1987 PerthWA6845 Australia Coriolis Pharma Fraunhoferstrasse 18b Martinsried82152 Germany School of Pharmaceutical Sciences University of Geneva Rue Michel-Servet 1 GenevaCH-1206 Switzerland Institute of Pharmaceutical Sciences of Western Switzerland University of Geneva GenevaCH-1206 Switzerland Swiss Bioinformatics Institute University of Geneva GenevaCH-1206 Switzerland Via Celoria 26 Milan20133 Italy Department of Biomolecular Sciences University of Urbino "Carlo Bo" Piazza Rinascimento 6 Urbino61029 Italy Department of Pharmacy and Biotechnology Alma Mater Studiorum University of Bologna Via Belmeloro 6 Bologna40126 Italy Atomistic Simulations Italian Institute of Technology Genova Italy Yusuf Hamied Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Institute for Bioinnovation Biomedical Sciences Research Center "Alexander Fleming" Vari16672 Greece Laboratory of Computational Science and Modeling Institut des Matériaux École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Chemistry University of California Davis DavisCA95616 United States Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States Center for Computational Biology Flatiron Institute New YorkNY10010 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States AI4Science Lab Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Computational Chemistry Group Van’t Hoff Institute for Molecular Sciences Unive

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce "PLUMED Tutorials", a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources. © 2024, CC BY.

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2D- and 3D-QSAR study of acyl homoserine lactone derivatives as potent inhibitors of quorum sensor, SdiA in Salmonella typhimurium

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International Journal Bioautomation 2016年第4期20卷 441-456页

作者： Shanmugam, Gnanendra Mohamed, Syed Natarajan, Jeyakumar Bioinformatics Division Center for Research and Development Mahendra Educational Institutions Mallasamudram Tiruchengode Tamil Nadu India Department of Molecular Modeling Sadakathullah Appa College Tirunelveli Tamil Nadu India Department of Bioinformatics Bharathiar University Coimbatore Tamil Nadu India

A series of Acyl homoserine lactone derivatives against quorum sensing (QS) enhanced transcriptional regulator SdiA of S. typhimurium were used to establish the physicochemical and structural requirements for the inhibition of QS using 2D- and 3D-QSAR methods. The QSAR model was developed by employing 35 compounds as a training set and the predictive ability was assessed by a test set of 12 compounds. The best 2D-QSAR model for the prediction of SdiA, quorum sensor inhibitory activity has been developed using Multiple Linear Regression (MLR) method (giving r2 = 0.8012 and q2 = 0.657), Principal Component Regression (PCR) method (giving r2 = 0.8104 and q2 = 0.625), and Partial Least Squares Regression (PLS) method (giving r2 = 0.8023 and q2 = 0.648). The best model for 3D-QSAR has been obtained using Comparative Molecular Field Analysis (CoMFA) method, giving r2 = 0.896 and q2 = 0.772. The 2D-QSAR results revealed that the most important descriptors for predicting the anti-quorum sensing activity were alignment-independent descriptors and the topology index descriptors. The 3D-QSAR results of CoMFA contour maps impart some important structural features-like electronegative substituent (Br, Cl, F) on lactone ring favors the strong inhibitory activity. These results will be further useful for development of new quorum sensing inhibitors with structural diversity.

关键词： Computational chemistry

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On Using Physico-Chemical Properties of Amino Acids in String Kernels for Protein Classification via Support Vector Machines

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Journal of Systems Science & Complexity 2015年第2期28卷 504-516页

作者： LI Limin AOKI-KINOSHITA Kiyoko F CHING Wai-Ki JIANG Hao Institute of Information and System Science Xi'an Jiaotong University Department of Bioinformatics Faculty of EngineeringSoka University Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Department of Mathematics School of InformationRenmin University of China

String kernels are popular tools for analyzing protein sequence data and they have been successfully applied to many computational biology problems. The traditional string kernels assume that different substrings are independent. However, substrings can be highly correlated due to their substructure relationship or common physico-chemical properties. This paper proposes two kinds of weighted spectrum kernels： The correlation spectrum kernel and the AA spectrum kernel. We evMuate their performances by predicting glycan-binding proteins of 12 glycans. The results show that the correlation spectrum kernel and the AA spectrum kernel perform significantly better than the spectrum kernel for nearly all the 12 glycans. By comparing the predictive power of AA spectrum kernels constructed by different physico-chemical properties, the authors can also identify the physico- chemical properties which contributes the most to the glycan-protein binding. The results indicate that physico-chemical properties of amino acids in proteins play an important role in the mechanism of glycamprotein binding.

关键词： AAindex AA spectrum kernel correlation spectrum kernel physico-chemical properties string kernel weighted spectrum kernel.

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Editorial

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Fuzzy Sets and Systems 2015年 281卷 1-3页

作者： De Baets, Bernard Dubois, Didier Hüllermeier, Eyke KERMIT Dept. of Math. Modeling Statistics and Bioinformatics Ghent University Belgium IRIT CNRS Université de Toulouse France Intelligent Systems Group Department of Computer Science University of Paderborn Germany

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Publisher Correction: Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly

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Nature communications 2019年第1期10卷 2459页

作者： Peer Aramillo Irizar Sascha Schäuble Daniela Esser Marco Groth Christiane Frahm Steffen Priebe Mario Baumgart Nils Hartmann Shiva Marthandan Uwe Menzel Jule Müller Silvio Schmidt Volker Ast Amke Caliebe Rainer König Michael Krawczak Michael Ristow Stefan Schuster Alessandro Cellerino Stephan Diekmann Christoph Englert Peter Hemmerich Jürgen Sühnel Reinhard Guthke Otto W Witte Matthias Platzer Eytan Ruppin Christoph Kaleta Research Group Medical Systems Biology Institute of Experimental Medicine Christian-Albrechts-University Kiel D-24105 Kiel Germany. Jena University Language and Information Engineering Lab Friedrich-Schiller-University Jena D-07743 Jena Germany. GerontoSys JenAge Consortium D-07745 Jena Germany. Genome Analysis Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Hans Berger Department of Neurology Jena University Hospital D-07747 Jena Germany. Systems Biology and Bioinformatics Group Leibniz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute D-07745 Jena Germany. Biology of Ageing Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Molecular Genetics Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Imageing Facility Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Integrated Research and Treatment Center Center for Sepsis Control and Care (CSCC) Jena University Hospital D-07747 Jena Germany. Network Modeling Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute D-07745 Jena Germany. Institute for Medical Informatics and Statistics Christian-Albrechts-University Kiel D-24105 Kiel Germany. Energy Metabolism Laboratory Swiss Federal Institute of Technology (ETH) Zurich Schwerzenbach/Zürich CH-8603 Switzerland. Department of Bioinformatics Friedrich-Schiller-University Jena D-07743 Jena Germany. Laboratory of Neurobiology Scuola Normale Superiore University of Pisa I-56100 Pisa Italy. Molecular Biology Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Faculty of Biology and Pharmacy Friedrich-Schiller-University Jena D-07743 Jena Germany. Biocomputing Lab Leibniz Institute on Aging-Fritz-Lipmann-Institute D-07745 Jena Germany. Department of Computer Science and Center for Bioinformatics and Computational Biology University of Maryland College Park M

The original version of this Article contained an error in the spelling of the author Jule Müller, which was incorrectly given as Julia Müller. Additionally, in Fig. 4a, the blue-red colour scale for fold change in ageing/disease regulation included a blue stripe in place of a red stripe at the right-hand end of the scale. These errors have been corrected in both the PDF and HTML versions of the Article.

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Molecular modeling of complete tertiary structure of pyrin and influence of mutations on it

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Pediatric Rheumatology 2015年第1期13卷 O14-O14页

作者： Arakelov, G. Nazaryan, K. Russian-Armenian (Slavonic) University Bioengineering and Bioinformatics Yerevan Armenia Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia Laboratory of Computational Modeling of Biological Processes Yerevan Armenia

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