2025
Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement
Kollapally N, Geller J, Keloth V, He Z, Xu J. Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement. Journal Of Biomedical Informatics 2025, 168: 104865. PMID: 40543734, DOI: 10.1016/j.jbi.2025.104865.Peer-Reviewed Original ResearchSemantic triplesSeed ontologyHuman expertsLogical axiomsPubMed abstractsSimilarity search techniquesState-of-the-artReal-world conceptsNetwork-based filterLanguage modelSemantic correctnessText corpusNetwork-based searchSource of textDomain viewSearch techniqueNetwork-based similaritySemMedDBOntology toolsOntologyIdentified conceptsSource of conceptsPipelineDomains of social determinants of healthAxiomsPreventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse
Simson J, Draxler F, Mehr S, Kern C. Preventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse. 2025, 1-30. DOI: 10.1145/3706598.3713482.Peer-Reviewed Original ResearchMachine learningCentral design decisionsDesign decisionsData practicesModel building pipelineML pipelineInherent trade-offIterative developmentCitizen science platformScience platformSystem outputInputTrade-OffsPrivacyDiverse stakeholdersPeople's inputDecisionPipelinePublic participationParticipatory inputImproving topic modeling performance on social media through semantic relationships within biomedical terminology
Xin Y, Grabowska M, Gangireddy S, Krantz M, Kerchberger V, Dickson A, Feng Q, Yin Z, Wei W. Improving topic modeling performance on social media through semantic relationships within biomedical terminology. PLOS ONE 2025, 20: e0318702. PMID: 39982945, PMCID: PMC11845042, DOI: 10.1371/journal.pone.0318702.Peer-Reviewed Original ResearchConceptsSocial media textsTopic modelsSocial mediaHealth-related topicsAnalyze social mediaSemantic relationshipsBiomedical terminologiesMedical conceptsSemantic typesRecord validationModeling pipelineMedia textsUnsupervised machineExpert evaluationHealthcare ResearchModel performanceOnline discussionsTextTopicsPipelineUsersMachineModeling approachModelTechnique's potential
2024
Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels
Spies N, Militello L, Farnsworth C, El-Khoury J, Durant T, Zaydman M. Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels. Clinical Chemistry 2024, 71: 296-306. PMID: 39545815, DOI: 10.1093/clinchem/hvae168.Peer-Reviewed Original ResearchSHapley Additive exPlanationsLearning approachDetection of contamination eventsUnsupervised learning approachLearning-based methodsMachine learning-based methodsEnsemble learning approachMachine learning pipelineEnsemble learningLearning pipelineMatthews correlation coefficientAlgorithmic fairnessReal worldSHapley Additive exPlanations valuesCurrent workflowsClinical workflowWorkflowOperational burdenBasic metabolic panelIntravenous (IVPipelineInternal validation setValidation setFlagging ratesPerformance assessmentParameter optimization for stable clustering using FlowSOM: a case study from CyTOF
Tao W, Sinha A, Raddassi K, Pandit A. Parameter optimization for stable clustering using FlowSOM: a case study from CyTOF. Frontiers In Immunology 2024, 15: 1414400. PMID: 39445014, PMCID: PMC11497637, DOI: 10.3389/fimmu.2024.1414400.Peer-Reviewed Original ResearchParameter optimizationMachine learning methodologyMachine learningComplex dataClustering outcomesLearning methodologyAssociated with immune disordersModified pipelineCell phenotypeImmune cell populationsDatasetBugsAutomated gatingOptimizationMachineImmunological datasetsImmune disordersScalabilityStable clustersCell populationsPipelineCellular changesCase studyCyTOFCyTOF dataAnalysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition
Faghiri A, Iraji A, Adali T, Calhoun V. Analysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition. 2024, 00: 13346-13350. DOI: 10.1109/icassp48485.2024.10446864.Peer-Reviewed Original ResearchStandardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach.
Zuo X, Zhou Y, Duke J, Hripcsak G, Shah N, Banda J, Reeves R, Miller T, Waitman L, Natarajan K, Xu H. Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach. AMIA Annual Symposium Proceedings 2024, 2023: 834-843. PMID: 38222429, PMCID: PMC10785935.Peer-Reviewed Original Research
2023
Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach
Hu Y, Keloth V, Raja K, Chen Y, Xu H. Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach. Bioinformatics 2023, 39: btad542. PMID: 37669123, PMCID: PMC10500081, DOI: 10.1093/bioinformatics/btad542.Peer-Reviewed Original ResearchNatural language processingMicro-F1 scoreCOVID-19 datasetNLP pipelineF1 scoreEntity recognition modelAD datasetPICO elementsSentence classificationNER modelRecognition modelLanguage processingLearning approachLearning modelEnd evaluationSupplementary dataDatasetPipelineExtractionInformationRCT abstractsAnnotationSentencesBioinformaticsComplexity
2022
Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging
Kong H, Kim J, Moon H, Park H, Kim J, Lim R, Woo J, Fakhri G, Kim D, Kim S. Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging. Scientific Reports 2022, 12: 18118. PMID: 36302815, PMCID: PMC9613909, DOI: 10.1038/s41598-022-22222-z.Peer-Reviewed Original ResearchConceptsSynthetic data augmentationData augmentationLack of training dataConventional data augmentationDeep learning methodsTraining dataLearning methodsPipeline approachAlgorithm trainingGraphical dataAutomationWaters' view radiographsModel performanceAutomated pipelinePerformancePerformance parametersAlgorithmDatasetAugmentationDataMethodPipelineRulesIndustrial workersTidyMass an object-oriented reproducible analysis framework for LC–MS data
Shen X, Yan H, Wang C, Gao P, Johnson CH, Snyder MP. TidyMass an object-oriented reproducible analysis framework for LC–MS data. Nature Communications 2022, 13: 4365. PMID: 35902589, PMCID: PMC9334349, DOI: 10.1038/s41467-022-32155-w.Peer-Reviewed Original ResearchConceptsObject-oriented computational frameworkComputational frameworkExtensible toolMetabolomics data analysisData structureWorkflow needsModular architectureOwn pipelinesData processingMultiple toolsAnalysis frameworkDesign philosophyR packageFrameworkLC-MS dataData analysisToolUsersArchitectureTraceabilityPipelineProcessingPackageGrammarApplying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry
Rouzrokh P, Khosravi B, Johnson Q, Faghani S, Garcia D, Erickson B, Kremers H, Taunton M, Wyles C. Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry. Journal Of Bone And Joint Surgery 2022, 104: 1649-1658. PMID: 35866648, PMCID: PMC9617138, DOI: 10.2106/jbjs.21.01229.Peer-Reviewed Original ResearchConceptsExtraction of image dataDeep learning algorithmsDeep learning pipelineRandom test sampleHuman annotatorsDICOM filesManual labelingDeep learningF1 scoreAlgorithm performancePixel dataAlgorithmEfficient pipelineMetadataFilesImage registryDICOMAnnotationAutomated pipelinePipelineHip radiographsRadiographic appearanceDeepImagesTotal hip arthroplasty
2021
TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
Gonzales R, Lamy J, Seemann F, Heiberg E, Onofrey J, Peters D. TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline. Lecture Notes In Computer Science 2021, 12906: 567-576. DOI: 10.1007/978-3-030-87231-1_55.Peer-Reviewed Original Research
2020
Developing staggered woven mesh aerator with three variable-micropore layers in recycling water pipeline to enhance CO2 conversion for improving Arthrospira growth
Cheng J, Liu S, Guo W, Song Y, Kumar S, Kubar AA, Su Y, Li Y. Developing staggered woven mesh aerator with three variable-micropore layers in recycling water pipeline to enhance CO2 conversion for improving Arthrospira growth. The Science Of The Total Environment 2020, 760: 143941. PMID: 33341634, DOI: 10.1016/j.scitotenv.2020.143941.Peer-Reviewed Original ResearchConceptsWater pipelinesArthrospira growthHigh-speed cameraLarge-scale raceway pondsCarbonization efficiencyInput COConversion efficiencySmall bubblesAeratorCO2 conversionUniform microporesRaceway pondsBubble picturesImage processing softwareUtilization efficiencyOptimized structureProcessing softwareEfficiencyLayerCOPipelineBiomass measurementsMicroporesShearBubbles
2019
An automated quality control pipeline for eQTL analysis with RNA-seq data
Wang T, Ruan J, Yin Q, Dong X, Wang Y. An automated quality control pipeline for eQTL analysis with RNA-seq data. 2019, 00: 1780-1786. DOI: 10.1109/bibm47256.2019.8983006.Peer-Reviewed Original ResearchExpression quantitative trait lociExpression quantitative trait loci analysisQuality control pipelineRNA-seqRNA-seq dataQuantitative trait lociTrait lociGenotype dataData normalization approachesControl pipelineGenotypesQuality of transcriptsComputational backgroundLociTranscriptionTraitsVariantsExpressionPipeline
2018
Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes
Shahbazi A, Kinnison J, Vescovi R, Du M, Hill R, Joesch M, Takeno M, Zeng H, da Costa NM, Grutzendler J, Kasthuri N, Scheirer WJ. Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes. Scientific Reports 2018, 8: 14247. PMID: 30250218, PMCID: PMC6155135, DOI: 10.1038/s41598-018-32628-3.Peer-Reviewed Original ResearchConceptsGigabytes of dataSupervised learning methodsHigh-energy synchrotron X-ray microtomographyHigh-quality reconstructionComputer visionHigh biological fidelityVirtual eyeSpectral confocal reflectance microscopyStack of imagesLearning methodsPipeline reconstructionContextual cluesDifferent modalitiesNeural volumeMouse datasetsBiological fidelityImagesData collectionPipelineGigabytesSegmentationMachineAlgorithmDatasetSufficient quality
2017
exprso: an R-package for the rapid implementation of machine learning algorithms
Quinn T, Tylee D, Glatt S. exprso: an R-package for the rapid implementation of machine learning algorithms. F1000Research 2017, 5: 2588. PMID: 29560250, PMCID: PMC5832912, DOI: 10.12688/f1000research.9893.2.Peer-Reviewed Original ResearchNon-expert programmersObject-oriented frameworkMulti-class classificationHigh-dimensional dataMachine learningEnsemble classificationIntuitive machineCross-validation schemeR packageFeature selectionNew R packageProgrammersInterchangeable modulesGeneralizable modelMachineClassificationRapid implementationModuleAlgorithmLearningImplementationPipelineSchemeFrameworkPredictionEnsembles of NLP Tools for Data Element Extraction from Clinical Notes.
Kuo T, Rao P, Maehara C, Doan S, Chaparro J, Day M, Farcas C, Ohno-Machado L, Hsu C. Ensembles of NLP Tools for Data Element Extraction from Clinical Notes. AMIA Annual Symposium Proceedings 2017, 2016: 1880-1889. PMID: 28269947, PMCID: PMC5333200.Peer-Reviewed Original ResearchConceptsNatural language processingNLP toolsElectronic health recordsData elementsConcept extractionLanguage processingEnsemble methodDiverse conceptsEvaluation resultsHealth recordsElement extractionClinical notesPlausible solutionToolPipelineExtractionPerformanceEnsembleExtraction performanceConceptNarrative textProcessingText
2012
An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection
Wang X, Kang DD, Shen K, Song C, Lu S, Chang LC, Liao SG, Huo Z, Tang S, Ding Y, Kaminski N, Sibille E, Lin Y, Li J, Tseng GC. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 2012, 28: 2534-2536. PMID: 22863766, PMCID: PMC3463115, DOI: 10.1093/bioinformatics/bts485.Peer-Reviewed Original ResearchConceptsDifferent operation systemsMulti-core parallel computingUser-friendly softwareParallel computingPathway detectionSoftware suiteFlexible inputFast implementationOperation systemVisualization plotsSupplementary dataNew algorithmMetapathsNew challengesSummary outputMarker detectionPathway databasesLittle effortMeta-analysis pipelineRapid advancesHigh-throughput genomic technologiesGenomic dataSystematic pipelineComputingPipeline
2007
Tilescope: online analysis pipeline for high-density tiling microarray data
Zhang ZD, Rozowsky J, Lam H, Du J, Snyder M, Gerstein M. Tilescope: online analysis pipeline for high-density tiling microarray data. Genome Biology 2007, 8: r81. PMID: 17501994, PMCID: PMC1929149, DOI: 10.1186/gb-2007-8-5-r81.Peer-Reviewed Original Research
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