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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Conditions of the fuzzy internet mortgage market submodels implementation

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2012年 243卷 1470-1479页

作者： Orlowski, Aleksander Szczerbicki, Edward Gdansk University of Technology Faculty of Applied Physics Mathematics PhD Program Poland Gdansk University of Technology Faculty of Management and Economics Poland

ISBN: (纸本)9781614991045

The paper introduces some challenges of the fast growing mortgage market in Poland, being also the next step of research presented in previous publications, showing the complete process of building and verifying three submodels for the internet mortgage market. The sub-models are described for three market situations, i.e. stable market, crisis, and boom, then the variables within the models are analyzed statistically (e.g. correlation level) and finally the models are verified. The paper presents the process of verification of submodel nr 3 (fast growing market) as an example of the verification process that was applied. The paper concludes with directions for further research in this area leading to the ultimate application of the proposed model in a real life prognosis process. © 2012 The authors and IOS Press. All rights reserved.

关键词： Fuzzy logic

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Toward smart decision support system for Mortgage Market

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Systems Science 2010年第1期36卷 35-40页

作者： OrLowski, Aleksander Szczerbicki, Edward Gdańsk University of Technology Faculty of Applied Physics and Mathematics PhD Program Poland Gdańsk University of Technology Faculty of Management and Economics Poland

This paper presents the structure of initial fuzzy model representing the Polish Internet Mortgage Market. It starts with an introduction describing the market complexities and challenges, and description of previously created rule based model. Then the steps of the process of proposed model fuzzification are presented. Next, there is presented Graphic User Interface developed for the model created. The final part of the paper consists of conclusions and directions for future research.

关键词： Decision support systems

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A weighted k-nearest neighbor method for gene ontology based protein function prediction

A weighted k-nearest neighbor method for gene ontology based...

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2nd International Multi-Symposiums on Computer and Computational Sciences 2007, IMSCCS'07

作者： Kharsikar, Saket Mugler, Dale Sheffer, Daniel Moore, Francisco Duan, Zhong-Hui Department of Biomedical Engineering University of Akron Akron OH 44236 United States Department of Theoretical and Applied Mathematics University of Akron Akron OH 44236 United States Integrated Bioscience PhD Program University of Akron Akron OH 44236 United States Department of Biology University of Akron Akron OH 44236 United States Department of Computer Science University of Akron Akron OH 44236 United States

ISBN: (纸本)0769530397

Numerous genome projects have produced a large and ever increasing amount of genomic sequence data. However, the biological functions of many proteins encoded by the sequences remain unknown. Protein function annotation and prediction become an essential and challenging task of post-genomic research. In this paper, we present an automated protein function prediction system based on a set of proteins of known biological functions. The functions of the proteins are characterized with gene ontology (GO) annotations. The prediction system uses a novel measure to calculate the pair-wise overall similarity between protein sequences. The protein function prediction is performed based on the GO annotations of similar sequences using a weighted k-nearest neighbor method. We show the prediction accuracies obtained using the model organism yeast (Sacchyromyces cerevisiae). The results indicate that the weighted k-nearest neighbor method significantly outperforms the regular k-nearest neighbor method for protein molecular function prediction. © 2007 IEEE.

关键词： Proteins

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