2024
In silico model development and optimization of in vitro lung cell population growth
Mostofinejad A, Romero D, Brinson D, Marin-Araujo A, Bazylak A, Waddell T, Haykal S, Karoubi G, Amon C. In silico model development and optimization of in vitro lung cell population growth. PLOS ONE 2024, 19: e0300902. PMID: 38748626, PMCID: PMC11095723, DOI: 10.1371/journal.pone.0300902.Peer-Reviewed Original Research
2021
Identifiability of Infection Model Parameters Early in an Epidemic
Sauer T, Berry T, Ebeigbe D, Norton M, Whalen A, Schiff S. Identifiability of Infection Model Parameters Early in an Epidemic. SIAM Journal On Control And Optimization 2021, 0: s27-s48. PMID: 36338855, PMCID: PMC9634856, DOI: 10.1137/20m1353289.Peer-Reviewed Original ResearchA Markov random field model for network-based differential expression analysis of single-cell RNA-seq data
Li H, Zhu B, Xu Z, Adams T, Kaminski N, Zhao H. A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data. BMC Bioinformatics 2021, 22: 524. PMID: 34702190, PMCID: PMC8549347, DOI: 10.1186/s12859-021-04412-0.Peer-Reviewed Original ResearchConceptsMarkov random field modelRandom field modelMean field-like approximationField modelSpecific DEGsExpectation maximizationSingle-cell sequencing technologiesProtein-coding genesRNA sequencing data setsSingle-cell RNA-seq dataCell-type levelCell typesGibbs samplerSingle-cell RNA sequencing data setsCell-cell networksDifferential expression analysisRNA-seq dataGene network informationStatistical powerType I error ratesDifferent expression levelsMRF modelI error rateModel parametersBiological networks
2020
Evaluating Domestic Well Vulnerability to Contamination From Unconventional Oil and Gas Development Sites
Soriano M, Siegel H, Gutchess K, Clark C, Li Y, Xiong B, Plata D, Deziel N, Saiers J. Evaluating Domestic Well Vulnerability to Contamination From Unconventional Oil and Gas Development Sites. Water Resources Research 2020, 56 DOI: 10.1029/2020wr028005.Peer-Reviewed Original ResearchPhase contaminationUnconventional oilSingle calibrated modelAqueous phase contaminantsWell vulnerabilityGas developmentGas development sitesCapture zoneMatrix hydraulic conductivitySetback distanceProbabilistic capture zonesHydraulic fracturingRatio of fractureMatrix conductivityHorizontal drillingSurface spillsFlow pathsCalibrated modelHydraulic conductivityPad locationsTransport timescalesDomestic groundwater wellsConductivityGroundwater contaminationModel parameters
2019
Analyzing collective motion with machine learning and topology
Bhaskar D, Manhart A, Milzman J, Nardini JT, Storey KM, Topaz CM, Ziegelmeier L. Analyzing collective motion with machine learning and topology. Chaos An Interdisciplinary Journal Of Nonlinear Science 2019, 29: 123125. PMID: 31893635, PMCID: PMC7027427, DOI: 10.1063/1.5125493.Peer-Reviewed Original ResearchSupervised machine learning methodsMachine learning methodsTopological data analysisCollective motionMachine learningLearning methodsOrder parameterSimulation dataTopological approachPersistent homologyTraditional order parametersPrior knowledgeMachineCollective behaviorModel parametersData analysisLarge librariesNumerical simulationsTopologyMultiple scalesMotionTime seriesLearningDifferent typesParametersNetPyNE, a tool for data-driven multiscale modeling of brain circuits
Dura-Bernal S, Suter BA, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd GM, Lytton WW. NetPyNE, a tool for data-driven multiscale modeling of brain circuits. ELife 2019, 8: e44494. PMID: 31025934, PMCID: PMC6534378, DOI: 10.7554/elife.44494.Peer-Reviewed Original ResearchConceptsInformation-theoretic measuresDeclarative languageImplementation codeGraphical interfaceNetPyNENetwork parametersTheoretic measuresNetwork modelNeuron networkStandardized formatUsersMillions of cellsConnectivity rulesExperimental datasetsNetworkRaster plotMultiscale network modelMultiple scalesDatasetToolModelersSpecificationModel parametersModelingVisualization
2016
A Statistical Model to Analyze Clinician Expert Consensus on Glaucoma Progression using Spatially Correlated Visual Field Data
Warren JL, Mwanza JC, Tanna AP, Budenz DL. A Statistical Model to Analyze Clinician Expert Consensus on Glaucoma Progression using Spatially Correlated Visual Field Data. Translational Vision Science & Technology 2016, 5: 14-14. PMID: 27622079, PMCID: PMC5017314, DOI: 10.1167/tvst.5.4.14.Peer-Reviewed Original ResearchStatistical modelSpatial probit regression modelsDeviance information criterionModel selection metricsBayesian settingSimulation study resultsModel parametersInformation criterionSpatial modelingCorrelated sensitivityNew methodologySingle frameworkSelection metricsField dataModelProbit regression modelInferenceEstimationVF locationsRegression modelsModelingPredictive abilityNumber of areasEstimating the Size of a Large Network and its Communities from a Random Sample.
Chen L, Karbasi A, Crawford FW. Estimating the Size of a Large Network and its Communities from a Random Sample. Advances In Neural Information Processing Systems 2016, 29: 3072-3080. PMID: 28867924, PMCID: PMC5578631.Peer-Reviewed Original ResearchStochastic block modelMost real-world networksImportant global propertiesLarge networksNumber of verticesReal-world networksRandom graphsBlock membershipGlobal propertiesSize estimation algorithmPartial informationEstimation algorithmModel parametersBlock modelInduced subgraphTheoretical analysisGlobal network propertiesVerticesNetwork propertiesComputer scienceTotal degreeEstimatorNetworkGraphSample size
2013
Bayesian shrinkage methods for partially observed data with many predictors
Boonstra P, Mukherjee B, Taylor J. Bayesian shrinkage methods for partially observed data with many predictors. The Annals Of Applied Statistics 2013, 7: 2272-2292. PMID: 24436727, PMCID: PMC3891514, DOI: 10.1214/13-aoas668.Peer-Reviewed Original ResearchFraction of missing informationOptimal bias-variance tradeoffBayesian shrinkage methodsEmpirical Bayes algorithmComprehensive simulation studyBias-variance tradeoffSurrogate covariatesSimulation studyShrinkage methodCovariatesPrediction problemState-of-the-artModel parametersProblemMissing DataLung cancer datasetBayes algorithmState-of-the-art technologiesArray technologyCancer datasetsQRT-PCRCreating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
Morris ED, Kim SJ, Sullivan JM, Wang S, Normandin MD, Constantinescu CC, Cosgrove KP. Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking. Journal Of Visualized Experiments 2013, 50358. PMID: 23963311, PMCID: PMC4046621, DOI: 10.3791/50358.Peer-Reviewed Original ResearchConceptsStatistical stepsTime-varying parametersTime-varying functionModel parametersSpatial filterDecomposition techniqueKinetic model parametersConventional modelsSpatial noiseFluctuationsNew modelParametersAnalysis techniquesLp-ntPETModelConventional methodsStatistical comparisonMain stepsDynamic PET data
2012
Reconstructing Mammalian Sleep Dynamics with Data Assimilation
Sedigh-Sarvestani M, Schiff S, Gluckman B. Reconstructing Mammalian Sleep Dynamics with Data Assimilation. PLOS Computational Biology 2012, 8: e1002788. PMID: 23209396, PMCID: PMC3510073, DOI: 10.1371/journal.pcbi.1002788.Peer-Reviewed Original ResearchConceptsUnscented Kalman filterData assimilationData assimilation frameworkParameter estimation methodNonlinear computational modelSleep-wake regulatory networkAssimilation frameworkUnknown parametersHidden variablesCovariance inflationNoisy variablesSlow dynamicsSparse measurementsComputational modelModel parametersKalman filterModel statesEstimation methodSimulation studyComplex systemsOptimal variablesFilter modelUKF frameworkModel variablesPartial observability
2011
Biomedical Model Fitting and Error Analysis
Costa KD, Kleinstein SH, Hershberg U. Biomedical Model Fitting and Error Analysis. Science Signaling 2011, 4: tr9. PMID: 21954296, PMCID: PMC3272496, DOI: 10.1126/scisignal.2001983.Peer-Reviewed Original ResearchConceptsMathematical modelAppropriate mathematical modelModel parametersError analysisFit parameter valuesLinearization schemeNonlinear modelGoodness of fitNonlinear dataModel fittingBest fitParameter valuesInverse modelingComputational methodsParticular applicationSuch constantsExperimental dataFittingBiomedical systemsProblemFitModelParametersConstantsSeries of measurementsToward a Model-Based Predictive Controller Design in Brain–Computer Interfaces
Kamrunnahar M, Dias N, Schiff S. Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces. Annals Of Biomedical Engineering 2011, 39: 1482-1492. PMID: 21267657, PMCID: PMC3655721, DOI: 10.1007/s10439-011-0248-y.Peer-Reviewed Original ResearchConceptsModel-based predictive controllerController designPredictive controller designAR model parametersPredictive controllerFilter applicationsControllerInterface applicationsModel-based featuresModel parametersBrain-computer interface applicationsPerformanceApplicationsParametersDesignInterfaceBCI applications
2010
Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification
Ahn J, Mukherjee B, Gruber S, Sinha S. Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification. Biometrics 2010, 67: 546-558. PMID: 20560931, PMCID: PMC3119773, DOI: 10.1111/j.1541-0420.2010.01453.x.Peer-Reviewed Original ResearchConceptsStereotype regression modelSubtypes of casesDeletion of observationsExpectation/conditional maximization algorithmBaseline category logit modelEstimation of model parametersMissingness mechanismData mechanismCase-control dataProportional oddsBayesian approachCategorical responsesCase-control studyCase-control study of colorectal cancerMissingnessMaximization algorithmCategorical outcomesMonte CarloModel assumptionsRegression modelsStudy of colorectal cancerModel parametersNonidentifiabilityDisease subclassificationMultinomial logit model
2008
MSB: A mean-shift-based approach for the analysis of structural variation in the genome
Wang LY, Abyzov A, Korbel JO, Snyder M, Gerstein M. MSB: A mean-shift-based approach for the analysis of structural variation in the genome. Genome Research 2008, 19: 106-117. PMID: 19037015, PMCID: PMC2612956, DOI: 10.1101/gr.080069.108.Peer-Reviewed Original ResearchConceptsProbability density functionNumber of segmentsGood parameter initializationLikelihood functionArray CGH experimentsKernel-based approachUnderlying distributionModel parametersParameter initializationParticular assumptionsNonparametric methodsExpectation maximizationComputational methodsConvergenceGlobal criterionLocal gradients
2007
Biaxial Mechanics of Musculoskeletal Tissue and Fiber-Reinforced Scaffolds
O’Connell G, Sen S, Baker B, Mauck R, Elliott D. Biaxial Mechanics of Musculoskeletal Tissue and Fiber-Reinforced Scaffolds. 2007, 935-936. DOI: 10.1115/sbc2007-176540.Peer-Reviewed Original ResearchUniaxial testsFiber-reinforced tissuesBiaxial tensile testingBiaxial mechanical testingLarge aspect ratioBiaxial experimentsTensile testingBiaxial testsUniaxial behaviorMechanical testingBiaxial behaviorAspect ratioSitu geometryStrain configurationBoundary conditionsMusculoskeletal tissuesPrimary experimentsModel parametersLarge domainsBehaviorTest
2006
Combined Feature/Intensity-Based Brain Shift Compensation Using Stereo Guidance
DeLorenzo C, Papademetris X, Vives K, Spencer D, Duncan J. Combined Feature/Intensity-Based Brain Shift Compensation Using Stereo Guidance. 2006, 335-338. DOI: 10.1109/isbi.2006.1624921.Peer-Reviewed Original ResearchSoft tissue deformationBrain shift compensationImage-derived informationSurface displacementsTracking accuracySurface motionTissue deformationAppropriate model parametersShift compensationBrain motionReal surfacesBiomechanical modelStereo camera imagesModel parametersCompensation systemData tradeoffsBrain displacementDisplacementCamera imagesMotionDeformationAccuracyImage intensity
2002
Applying Hidden Markov Models to the Analysis of Single Ion Channel Activity
Venkataramanan L, Sigworth F. Applying Hidden Markov Models to the Analysis of Single Ion Channel Activity. Biophysical Journal 2002, 82: 1930-1942. PMID: 11916851, PMCID: PMC1301989, DOI: 10.1016/s0006-3495(02)75542-2.Peer-Reviewed Original ResearchConceptsCorrelated background noiseHidden Markov ModelDigital inverse filterMarkov model parametersDiscrete timeBaum-Welch algorithmMarkov modelComputational intensityDeterministic interferenceModel parametersInverse filterMultiple data setsAlgorithmPrevious resultsPractical applicationsNoise ratioState transitionsSharp frequencyData setsChannel dataSingle ion channel currentsNoiseExtensionRandomnessBackground noise
2001
Toward Quantitative Simulation of Germinal Center Dynamics: Biological and Modeling Insights from Experimental Validation
KLEINSTEIN S, SINGH J. Toward Quantitative Simulation of Germinal Center Dynamics: Biological and Modeling Insights from Experimental Validation. Journal Of Theoretical Biology 2001, 211: 253-275. PMID: 11444956, DOI: 10.1006/jtbi.2001.2344.Peer-Reviewed Original ResearchConceptsCenter dynamicsParticular mathematical modelOrdinary differential equationsGerminal center dynamicsImmune system dynamicsDifferential equationsExperimental dataMathematical modelStochastic frameworkAverage dynamicsSpecific experimental dataDeterministic modelSystem dynamicsModel parametersPossible extensionsGeneral methodologyQuantitative simulationOpreaNew implementationDynamicsModeling insightsPerelsonCenter behaviorEquationsExperimental validationSensitivity analysis and parameter identifiability for colloid transport in geochemically heterogeneous porous media
Sun N, Sun N, Elimelech M, Ryan J. Sensitivity analysis and parameter identifiability for colloid transport in geochemically heterogeneous porous media. Water Resources Research 2001, 37: 209-222. DOI: 10.1029/2000wr900291.Peer-Reviewed Original ResearchHeterogeneous porous mediaPorous mediaModel parametersParameter identificationFour-parameter setInverse solutionTracer breakthrough dataSubsurface porous mediaColloid transportParameter identifiabilityInverse problemTwo-dimensional modelEstimation errorLongitudinal dispersivityColloid deposition rate coefficientsSensitivity analysisKey model parametersDeposition rate coefficientsIdentifiabilityColloid transport modelSubsequent predictionTransport modelColloid depositionParametersRelease parameters
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