2024
Compartmentalized pooling generates orientation selectivity in wide-field amacrine cells
Lei W, Clark D, Demb J. Compartmentalized pooling generates orientation selectivity in wide-field amacrine cells. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2411130121. PMID: 39602271, PMCID: PMC11626119, DOI: 10.1073/pnas.2411130121.Peer-Reviewed Original ResearchConceptsOrientation selectivityBand-pass spatial frequency tuningVisual systemReceptive fieldsSpatial frequency tuningWide-field amacrine cellsReceptive field modelOrientation detectionKappa-opioid receptorsAmacrine cellsDetecting orientationVisual sceneFrequency tuningGlycinergic inhibitionOpioid receptorsField modelSpecific membrane resistanceExcitatory inputSynaptic inputsCalcium imagingMouse retinaCalcium signalingDendritic compartmentsMicrocircuit levelPolarization
2022
A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data
Zhu B, Li H, Zhang L, Chandra SS, Zhao H. A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. Briefings In Bioinformatics 2022, 23: bbac166. PMID: 35514182, PMCID: PMC9487630, DOI: 10.1093/bib/bbac166.Peer-Reviewed Original ResearchConceptsDE genesSeq dataSingle-cell RNA sequencing technologyDifferential expressionSingle-cell RNA-seq dataIdentification of genesRNA sequencing technologySpecific differential expressionSingle-cell resolutionRNA-seq dataMarkov random field modelMarkov random field model-based approachSimilar cell typesNovel statistical modelRandom field modelComplex biological systemsBiological pathwaysGene detectionGenesCell typesStatistical modelMouse datasetsField modelBiological systemsReal data
2021
A 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
2017
On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data
Lin Z, Wang T, Yang C, Zhao H. On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data. Biometrics 2017, 73: 769-779. PMID: 28099997, PMCID: PMC5515703, DOI: 10.1111/biom.12650.Peer-Reviewed Original ResearchConceptsGaussian graphical modelsTemporal dataGraphical modelsComplex data structuresJoint estimationMarkov random field modelRandom field modelParallel computingSelection consistencyData structureStatistical inferenceNeighborhood selection methodTemporal dependenciesEfficient algorithmIndividual networksMultiple groupsSpatial dataModel convergesNetwork estimationField modelSelection methodNetworkPosterior probabilitySimulation studyImproved estimation
2014
Conditional random fields for morphological analysis of wireless ECG signals
Natarajan A, Gaiser E, Angarita G, Malison R, Ganesan D, Marlin B. Conditional random fields for morphological analysis of wireless ECG signals. 2014, 2014: 370-379. PMID: 26726321, PMCID: PMC4697765, DOI: 10.1145/2649387.2649414.Peer-Reviewed Original ResearchECG signalsConditional random field modelOpen-source toolkitConditional Random FieldsMobile sensing technologiesIndependent prediction modelsRandom field modelSensor dataNew computational toolsSensing technologyRandom fieldsComputational toolsNovel approachField modelPrediction modelDiverse applicationsSame featuresNon-stationary signalsLab-based studiesUsersToolkitProblemTechnologyFrameworkCapability
2011
Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies
Chen M, Cho J, Zhao H. Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies. PLOS Genetics 2011, 7: e1001353. PMID: 21490723, PMCID: PMC3072362, DOI: 10.1371/journal.pgen.1001353.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesBiological pathwaysSingle gene-based methodsMarkov random field modelGene-based methodsPrior biological knowledgeRandom field modelGWAS analysisAssociation signalsMultiple genesPathway topologyGene associationsAssociation analysisGenesBiological knowledgeField modelGenetic variantsSpecific pathwaysReal data examplePathwayStatistical inferenceConditional modes algorithmExchangeable setRegression form
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