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10th Latin American Symposium on Nuclear Physics and Applications, LASNPA 2013

作者： Márquez Damián, J.I. Granada, J.R. Malaspina, D.C. Departamento Física de Neutrones Instituto Balseiro Centro Atómico Bariloche Bariloche Argentina Computation Modeling and Bioinformatics Center Northwestern University EvanstonIL United States CONICET United States

During the past few years we have been working at the Department of Neutron Physics at Centro Atómico Bariloche on an update of the evaluation of the thermal neutron scattering law for light and heavy water. We based our approach on combining the best available experimental data with molecular dynamics simulations. With this methodology we produced a set of new thermal neutron scattering files in ENDF-6 format, that result an improvement over existing evaluations. In this paper we present the models, a brief summary of the validation and some applications to nuclear reactor calculations. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence.

关键词： Nuclear reactors

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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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Neural Network Models for Classifying Immune Cell Subsets

Neural Network Models for Classifying Immune Cell Subsets

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IEEE International Conference on bioinformatics and Biomedicine

作者： Yongguo Mei Raquel Hontecillas Xiaoying Zhang Adria Carbo Josep Bassaganya-Riera The Center for Modeling Immunity to Enteric Pathogens Nutritional Immunology and Molecular Medicine Laboratory Virginia Bioinformatics Institute Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9781479913114

The immune system is composed of heterogeneous cell populations and it includes several hundreds of distinct cells types such as neutrohils, eosinophils, basophils, macrophages, dendritic cells, CD4+ and CD8+ T cells, γδ T cells, mast cells, and B cells and each main cell type can be further differentiated into subsets with unique and overlapping functions. For example CD4+ T cells can be differentiated into T helper (Th)1, Th2, Thl7, and regulatory T cell (Treg) subsets. To study molecular mechanisms of cell differentiation, Systems Biology Markup Language (SBML) based Ordinary Differential Equation (ODE) models can be used for representing such processes. These intracellular signaling models often require many equations to accurately represent intracellular pathways and biochemical reactions. Another challenge in studying the immune system and immune responses is the need for integration of complex processes that occur at different time and space scales (i. e., populations, whole organism, tissue level, cellular and molecular) through multi-scale modeling. This study presents two novel neural network models for modeling CD4+ T cell differentiation and immune cell subset classification. The first model reduces the complex ODE intracellular model by focusing on the input and output cytokines and the second model establishes an automated subset classification based on molecular patterns expressed in immune cells. After training, the first model achieves small prediction errors of cytokine concentrations and the second model achieves 98% prediction rate for subset classification. Neural network algorithm and models have been widely used for many data mining tasks such as classification and pattern recognition. However, to the best of our knowledge this study is the first one applying the neural network algorithm for immune cell differentiation and subset classification. In addition, these novel neural network models can be computationally efficiently integrated into m

关键词： Classifying Immune ODE SBML

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186 Molecular dynamics and ligand based studies for the validation of potential inhibitors for SrtA againstBacillus anthracis

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Journal of Biomolecular Structure and Dynamics 2013年第SUPP 1期31卷 119-120页

作者： Sanjeev Kumar Singh[a][*] & Chandrabose Selvaraj[a] [a] Computer Aided Drug Design and Molecular Modeling Lab Department of Bioinformatics Alagappa University Karaikudi 630003 India Phone: Phone: +91-4565-230725 Fax: Phone: +91-4565-230725

SrtA enzymes cleave the sorting signals of secreted proteins to form isopeptide (amide) bonds between the secreted proteins and peptidoglycan or polypeptides to function as the principal architects of theBacillus anthracis. Inhibition of SrtA can completely eradicate the growth ofB. anthracisdue to the lack of signals and so the SrtA is universally accepted as the drug target for all gram positive pathogens. Here, with the reported compounds, the pharmacophore-based virtual screening protocol has been used to obtain similar pharmacological property that derived new compounds to inhibit the SrtA structure. New compounds on AAAHR Pharmacophore hypothesis screening were treated with four phase of docking protocols with combined Glide-QPLD docking approach and charge accuracy variations were dominated by QM/MM approach. Finally, the compounds of 19941, 14704, 121962, 20137, and 19533 from binding db, Chembridge db, and Toslab were succeeded from the screening; these compounds are having better scoring, energy parameters, ADME physio/chemical properties, tendency to transfer the electrons between the protein–ligand interactions, and these compounds also predict having cell adhesion inhibitory activity. These screened compounds have better stability at active site loop regions with strong bonding interactions and these compounds on clinical trials will definitely emerge as better SrtA inhibitor against theB. anthracis.

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Generalized antisymmetric filters for edge detection

Generalized antisymmetric filters for edge detection

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International Conference of Soft Computing and Pattern Recognition (SOCPAR)

作者： Nicolás Madrid Carlos Lopez-Molina Bernard De Baets Institute for Research and Applications of Fuzzy Modeling University of Ostrava Ostrava Czech Republic Dpto. Automatica y Computacion Universidad Publica de Navarra Pamplona Spain Dept. of Mathematical Modeling Statistics and Bioinformatics Ghent University Gent Belgium

A large number of filters has been proposed to compute local gradients in grayscale images, usually having as goal the adequate characterization of edges. A significant portion of such filters are antisymmetric with respect to the origin. In this work we propose to generalize those filters by incorporating an explicit evaluation of the tonal difference. More specifically, we propose to apply restricted dissimilarity functions to appropriately measure the tonal differences. We present the mathematical developments, as well as quantitative experiments that indicate that our proposal offers a clear option to improve the performance of classical edge detection filters.

关键词： Image edge detection Convolution Proposals Feature extraction Computational modeling Approximation methods Pattern recognition

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Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

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BMC bioinformatics 2016年第3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca USA Graduate Field of Computational Biology Cornell University Ithaca USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Antwerpen Belgium Department of Computer Science University of Arizona Tucson USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Minds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai India Philips Research North America Briarcliff Manor USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Tokyo Japan Systems Biology Centre University of Warwick Senate House Coventry UK Systems Biology Doctoral Training Centre University of Warwick Senate House Coventry UK Center for B

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

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Concurrent algorithm for learning of neural networks

Concurrent algorithm for learning of neural networks

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2012 6th IEEE International Conference Intelligent Systems, IS 2012

作者： Antonov, Anton Hadjitodorov, Stefan Bioinformatics and Mathematical Modeling IBPhBME-Bulgarian Academy of Sciences Sofia Bulgaria

The purpose of the paper is to present an algorithm for concurrent processing on multi-layer neural networks and its advantages in the context of development of parallel computing systems and clouds. © 2012 IEEE.

ISBN: (纸本)9781467327824

关键词： Network layers

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modeling GENETIC REGULATORY NETWORKS:A DELAY DISCRETE DYNAMICAL MODEL APPROACH

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Journal of Systems Science & Complexity 2012年第6期25卷 1052-1067页

作者： Hao JIANG Wai-Ki CHING Kiyoko F.AOKI-KINOSHITA Dianjing GUO Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Department of Bioinformatics Faculty of EngineeringSoka University Department of Biology The Chinese University of Hong Kong

modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.

关键词： Delay effect discrete dynamic system model genetic regulatory networks k-means clustering method linear multiple regression.

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Label ranking with partial abstention based on thresholded probabilistic models

Label ranking with partial abstention based on thresholded p...

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26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012

作者： Cheng, Weiwei Hüllermeier, Eyke Waegeman, Willem Welker, Volkmar Mathematics and Computer Science Philipps-Universität Marburg Marburg Germany Mathematical Modeling Statistics and Bioinformatics Ghent University Ghent Belgium

ISBN: (纸本)9781627480031

Several machine learning methods allow for abstaining from uncertain predictions. While being common for settings like conventional classification, abstention has been studied much less in learning to rank. We address abstention for the label ranking setting, allowing the learner to declare certain pairs of labels as being incomparable and, thus, to predict partial instead of total orders. In our method, such predictions are produced via thresholding the probabilities of pairwise preferences between labels, as induced by a predicted probability distribution on the set of all rankings. We formally analyze this approach for the Mallows and the Plackett-Luce model, showing that it produces proper partial orders as predictions and characterizing the expressiveness of the induced class of partial orders. These theoretical results are complemented by experiments demonstrating the practical usefulness of the approach.

关键词： Forecasting

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Physicochemical properties of exogenous molecules correlated with their biological efficacy as protectors against carcinogenesis and inflammation (vol 32, pg 393, 2013)

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INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY 2013年第3期32卷 I-I页

作者： Bensasson, Rene V. Sowlati-Hashjin, Shahin Zoete, Vincent Dauzonne, Daniel Matta, Cherif F. [a] Molécules de Communication et Adaptation des Microorganismes UMR 7245 CNRS/MNHN Muséum National d’Histoire Naturelle F-75005 Paris France [b] Department of Chemistry Mount Saint Vincent University Halifax Nova Scotia Canada B3M 2J6 [c] Department of Chemistry Saint Mary’s University Halifax Nova Scotia Canada B3H 3C3 [d] Molecular Modeling Group Swiss Institute of Bioinformatics CH-1015 Lausanne Switzerland [e] Institut Curie Section de Recherche 26 rue d’Ulm F-75005 Paris France [f] CNRS UMR 176 26 rue d’Ulm F-75005 Paris France

A large number of molecules referred to here as M, several of which are extracted from plants, belong to different chemical classes and exhibit chemoprotective activity against carcinogenesis and inflammation in rodents and in vitro cell culture. The aim of this article is to review and analyse our previous investigations on linear correlations observed between physicochemical parameters of the tested molecules M and their biological efficacies measured in vivo and/or in vitro. The principal physicochemical parameters studied characterise the energy required to oxidise M into M?+, E(M?+/M), namely its first vertical ionisation potential, and the energy liberated by the addition of an electron to M, i.e. its vertical electron affinity. These physicochemical properties were determined experimentally using diverse techniques and theoretically using mainly semiempirical (AM1) and density functional theory (DFT) quantum mechanical methods. View full text

关键词： cancer chemoprevention antiinflammation ionisation potential electron affinity quantum chemical calculations quantitative structure–activity relationships diphenols phenylpropenoids flavonoids triterpenoids phenols benzoic and salicylic acids

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