Search results for "Computer simulation"

showing 10 items of 1054 documents

Multiple testing in candidate gene situations: a comparison of classical, discrete, and resampling-based procedures.

2011

In candidate gene association studies, usually several elementary hypotheses are tested simultaneously using one particular set of data. The data normally consist of partly correlated SNP information. Every SNP can be tested for association with the disease, e.g., using the Cochran-Armitage test for trend. To account for the multiplicity of the test situation, different types of multiple testing procedures have been proposed. The question arises whether procedures taking into account the discreteness of the situation show a benefit especially in case of correlated data. We empirically evaluate several different multiple testing procedures via simulation studies using simulated correlated SN…

Statistics and ProbabilityCandidate geneContrast (statistics)computer.software_genrePolymorphism Single NucleotideSet (abstract data type)Computational MathematicsSample size determinationResamplingData Interpretation StatisticalSample SizeStatisticsMultiple comparisons problemGeneticsCochran–Armitage test for trendRange (statistics)HumansComputer SimulationDiseaseData miningMolecular BiologycomputerGenetic Association StudiesMathematicsStatistical applications in genetics and molecular biology
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TiFoSi: an efficient tool for mechanobiology simulations of epithelia

2020

[Motivation]: Emerging phenomena in developmental biology and tissue engineering are the result of feedbacks between gene expression and cell biomechanics. In that context, in silico experiments are a powerful tool to understand fundamental mechanisms and to formulate and test hypotheses.

Statistics and ProbabilityCell signalingCell divisionComputer scienceSystems biologyIn silicoCellBiophysicsMorphogenesisVertex ModelContext (language use)Computational biologyCleavage (embryo)BiochemistryEpitheliumFeedbackMechanobiologyEpithelia Simulation03 medical and health sciencesParacrine signallingMechanobiologyTissue engineeringMorphogenesismedicineComputer SimulationCellular dynamicsMolecular Biology030304 developmental biology0303 health sciencesSystems Biology030302 biochemistry & molecular biologyComputational BiologyCell cycleTissue SimulationJuxtacrine signallingComputer Science ApplicationsComputational Mathematicsmedicine.anatomical_structureComputational Theory and MathematicsDevelopmental biologyCell DivisionSoftwareDevelopmental BiologyBioinformatics
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Opportunities and challenges of combined effect measures based on prioritized outcomes

2013

Many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature. In case of binary or time-to-event variables, composite endpoints, which combine several event types within a single event or time-to-first-event analysis are often used to assess the overall treatment effect. A main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another. Recently, some authors proposed more general approaches based on a priority ranking of outcomes, which moreover allow to combine outcome variables of different scale levels. …

Statistics and ProbabilityClinical Trials as TopicEpidemiologyUnivariatecomputer.software_genreOutcome (game theory)Treatment OutcomeRankingScale (social sciences)Component (UML)Outcome Assessment Health CareMultiple comparisons problemHumansComputer SimulationData miningcomputerProportional Hazards ModelsMathematicsStatistical hypothesis testingEvent (probability theory)Statistics in Medicine
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Sparse relative risk regression models

2020

Summary Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios…

Statistics and ProbabilityClustering high-dimensional dataComputer sciencedgLARSInferenceScale (descriptive set theory)BiostatisticsMachine learningcomputer.software_genreRisk Assessment01 natural sciencesRegularization (mathematics)Relative risk regression model010104 statistics & probability03 medical and health sciencesNeoplasmsCovariateHumansComputer Simulation0101 mathematicsOnline Only ArticlesSurvival analysis030304 developmental biology0303 health sciencesModels Statisticalbusiness.industryLeast-angle regressionRegression analysisGeneral MedicineSurvival AnalysisHigh-dimensional dataGene expression dataRegression AnalysisArtificial intelligenceStatistics Probability and UncertaintySettore SECS-S/01 - StatisticabusinessSparsitycomputerBiostatistics
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Stochastic dynamics of leukemic cells under an intermittent targeted therapy

2009

The evolutionary dynamics of cancerous cell populations in a model of Chronic Myeloid Leukemia (CML) is investigated in the presence of an intermittent targeted therapy. Cancer development and progression is modeled by simulating the stochastic evolution of initially healthy cells which can experience genetic mutations and modify their reproductive behavior, becoming leukemic clones. Front line therapy for the treatment of patients affected by CML is based on the administration of tyrosine kinase inhibitors, namely imatinib (Gleevec) or, more recently, dasatinib or nilotinib. Despite the fact that they represent the first example of a successful molecular targeted therapy, the development o…

Statistics and ProbabilityComplex systemsmedicine.medical_treatmentModels BiologicalPiperazinesSettore FIS/03 - Fisica Della MateriaCancer evolutionTargeted therapyLeukemia Myelogenous Chronic BCR-ABL Positivehemic and lymphatic diseasesStochastic dynamics; Cancer evolution; Complex systemsHumansMedicineComputer SimulationStochastic dynamicMolecular Targeted TherapyProtein Kinase InhibitorsEcology Evolution Behavior and SystematicsStochastic Processesbusiness.industryApplied MathematicsMyeloid leukemiaImatinibmedicine.diseaseSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)DasatinibLeukemiaPyrimidinesImatinib mesylateNilotinibStochastic dynamics Monte Carlo simulationBenzamidesImmunologyCancer cellDisease ProgressionImatinib MesylateCancer researchbusinessmedicine.drug
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System size dependence of the autocorrelation time for the Swendsen-Wang Ising model

1990

Abstract We present Monte Carlo simulation results of the autocorrelation time for the Swendsen-Wang method for the simulation of the Ising model. We have calculated the exponential and the integrated autocorrelation time at the critical point T c of the two-dimensional Ising model. Our results indicate that both autocorrelation times depend logarithmically on the linear system size L instead of a power law. The simulations were carried out on the parallel computer of the condensed matter theory group at the University of Mainz.

Statistics and ProbabilityComputer simulationCritical point (thermodynamics)AutocorrelationMonte Carlo methodSquare-lattice Ising modelIsing modelStatistical physicsCondensed Matter PhysicsPower lawMathematicsExponential functionPhysica A: Statistical Mechanics and its Applications
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A weighted combined effect measure for the analysis of a composite time-to-first-event endpoint with components of different clinical relevance

2018

Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all-cause hazard ratio, which gives equal weight to all events irrespective of their type …

Statistics and ProbabilityHazard (logic)EpidemiologyEndpoint Determination01 natural sciencesMeasure (mathematics)WIN RATIO010104 statistics & probability03 medical and health sciences0302 clinical medicineResamplingStatisticstime-to-eventHumansComputer Simulation030212 general & internal medicinerelevance weighting0101 mathematicsParametric statisticsEvent (probability theory)MathematicsProportional Hazards Modelsclinical trialsHazard ratiocomposite endpointWeightingPRIORITIZED OUTCOMESTRIALSData Interpretation StatisticalMULTISTATE MODELSINFERENCENull hypothesisMonte Carlo MethodStatistics in Medicine
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Sparse kernel methods for high-dimensional survival data

2008

Abstract Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be ‘kernelized’. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, dependin…

Statistics and ProbabilityLung NeoplasmsLymphomaComputer sciencecomputer.software_genreComputing MethodologiesBiochemistryPattern Recognition AutomatedArtificial IntelligenceMargin (machine learning)CovariateCluster AnalysisHumansComputer SimulationFraction (mathematics)Molecular BiologyProportional Hazards ModelsModels StatisticalTraining setProportional hazards modelGene Expression ProfilingComputational BiologyComputer Science ApplicationsSupport vector machineComputational MathematicsKernel methodComputational Theory and MathematicsRegression AnalysisData miningcomputerAlgorithmsSoftwareBioinformatics
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Stability of a stochastic SIR system

2005

Abstract We propose a stochastic SIR model with or without distributed time delay and we study the stability of disease-free equilibrium. The numerical simulation of the stochastic SIR model shows that the introduction of noise modifies the threshold of system for an epidemic to occur and the threshold stochastic value is found.

Statistics and ProbabilityLyapunov functionStochastic stabilityComputer simulationStochastic processComputer Science::Social and Information NetworksCondensed Matter PhysicsStability (probability)Noise (electronics)SIR model Lyapunov function Stochastic process Stochastic stabilitysymbols.namesakeControl theorysymbolsQuantitative Biology::Populations and EvolutionApplied mathematicsEpidemic modelMathematicsPhysica A: Statistical Mechanics and its Applications
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Numerical simulation of creeping fluid flow in reconstruction models of porous media

2002

Abstract In this paper we examine representative examples of realistic three-dimensional models for porous media by comparing their geometry and permeability with those of the original experimental specimen. The comparison is based on numerically exact evaluations of permeability, porosity, specific internal surface, mean curvature, Euler number and local percolation probabilities. The experimental specimen is a three-dimensional computer tomographic image of Fontainebleau sandstone. The three models are stochastic reconstructions for which many of the geometrical characteristics coincide with those of the experimental specimen. We find that in spite of the similarity in the geometrical pro…

Statistics and ProbabilityMean curvatureComputer simulationGeometryMechanicsPhysics::Classical PhysicsCondensed Matter PhysicsPhysics::GeophysicsPermeability (earth sciences)Fluid dynamicsComputer tomographicPorous mediumPorosityMathematicsPhysica A: Statistical Mechanics and its Applications
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