2023
From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights
Hänsel K, Dudgeon S, Cheung K, Durant T, Schulz W. From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights. Journal Of Medical Systems 2023, 47: 65. PMID: 37195430, PMCID: PMC10191934, DOI: 10.1007/s10916-023-01951-2.Commentaries, Editorials and LettersMeSH KeywordsAlgorithmsBiomedical ResearchHumansPattern Recognition, AutomatedPhenotypePrecision MedicineConceptsKnowledge graphReal-world dataGraph modelBiomedical data integrationGraph data modelIntegrated Knowledge GraphBiomedical knowledge graphsElectronic health recordsData integrationData insightsData modelInsight generationBiomedical informationHealth recordsArt researchGraphNovel approachCombination of dataDisease phenotypingInformationPrecision medicine researchHealthcareIntegrationEHRIntriguing opportunity
2020
More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses
Dong GH, Wang Z, Dong H, Wang M, Zheng Y, Ye S, Zhang J, Potenza MN. More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses. Journal Of Behavioral Addictions 2020, 9: 642-653. PMID: 33031057, PMCID: PMC8943664, DOI: 10.1556/jba-9-642.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultBrainConnectomeDefault Mode NetworkDiagnostic and Statistical Manual of Mental DisordersExecutive FunctionFemaleHumansImage Interpretation, Computer-AssistedInternet Addiction DisorderMachine LearningMagnetic Resonance ImagingMaleMultivariate AnalysisNerve NetPattern Recognition, AutomatedRecreationVideo GamesYoung AdultManual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI
McGrath H, Li P, Dorent R, Bradford R, Saeed S, Bisdas S, Ourselin S, Shapey J, Vercauteren T. Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI. International Journal Of Computer Assisted Radiology And Surgery 2020, 15: 1445-1455. PMID: 32676869, PMCID: PMC7419453, DOI: 10.1007/s11548-020-02222-y.Peer-Reviewed Original ResearchConceptsManual segmentationHigh quality softwareTime-intensive taskGeneric softwareIntensive tasksSegmentation accuracySegmentation timeSegmentation approachVestibular schwannomaSegmentationSegmentation effortsApplicable solutionSoftwareReference approachCurrent clinical practiceVestibular schwannoma volumeAccuracyContrast agent injectionVolumetric measurementsEqual performanceTumor sizeVS sizeClinical practiceMore frustrationAgent injectionUncovering axes of variation among single-cell cancer specimens
Chen WS, Zivanovic N, van Dijk D, Wolf G, Bodenmiller B, Krishnaswamy S. Uncovering axes of variation among single-cell cancer specimens. Nature Methods 2020, 17: 302-310. PMID: 31932777, PMCID: PMC7339867, DOI: 10.1038/s41592-019-0689-z.Peer-Reviewed Original ResearchAlgorithmsAnimalsAntineoplastic AgentsBiopsyBreast NeoplasmsCluster AnalysisCytophotometryDrug Screening Assays, AntitumorEnzyme InhibitorsEpithelial-Mesenchymal TransitionFemaleHumansImage Interpretation, Computer-AssistedMammary Neoplasms, AnimalMiceNeoplasm MetastasisPattern Recognition, AutomatedPhenotypeRecombinant ProteinsSingle-Cell AnalysisSoftwareTransforming Growth Factor beta
2019
Multi‐site reproducibility of a human immunophenotyping assay in whole blood and peripheral blood mononuclear cells preparations using CyTOF technology coupled with Maxpar Pathsetter, an automated data analysis system
Bagwell CB, Hunsberger B, Hill B, Herbert D, Bray C, Selvanantham T, Li S, Villasboas JC, Pavelko K, Strausbauch M, Rahman A, Kelly G, Asgharzadeh S, Gomez‐Cabrero A, Behbehani G, Chang H, Lyberger J, Montgomery R, Zhao Y, Inokuma M, Goldberger O, Stelzer G. Multi‐site reproducibility of a human immunophenotyping assay in whole blood and peripheral blood mononuclear cells preparations using CyTOF technology coupled with Maxpar Pathsetter, an automated data analysis system. Cytometry Part B Clinical Cytometry 2019, 98: 146-160. PMID: 31758746, PMCID: PMC7543682, DOI: 10.1002/cyto.b.21858.Peer-Reviewed Original ResearchConceptsPeripheral blood mononuclear cellsWhole bloodPeripheral blood mononuclear cell preparationsDeep immune phenotypingBlood mononuclear cellsImmune cell populationsMononuclear cell preparationsCell populationsTranslational clinical researchWhole blood preparationsImmune phenotypingMononuclear cellsPBMC samplesClinical trialsImmune cellsClinical researchCyTOF technologyBlood preparationsInter-site reproducibilityBloodCell preparationsSingle donorMulti-site reproducibilityCellsProfiling assaysCommon spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface
Khalaf A, Sejdic E, Akcakaya M. Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface. Journal Of Neuroscience Methods 2019, 320: 98-106. PMID: 30946880, DOI: 10.1016/j.jneumeth.2019.03.018.Peer-Reviewed Original ResearchPIMKL: Pathway-Induced Multiple Kernel Learning
Manica M, Cadow J, Mathis R, Rodríguez Martínez M. PIMKL: Pathway-Induced Multiple Kernel Learning. Npj Systems Biology And Applications 2019, 5: 8. PMID: 30854223, PMCID: PMC6401099, DOI: 10.1038/s41540-019-0086-3.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBiomarkers, TumorComputational BiologyHumansMachine LearningPattern Recognition, AutomatedSoftwareConceptsMolecular interaction networksGene setsInteraction networkMolecular mechanismsState-of-the-art machine learning approachesTransfer learning tasksState-of-the-artMolecular signaturesCombination of kernelsMultiple Kernel LearningMachine learning applicationsIdentification of molecular biomarkersMolecular factorsMachine learning approachPhenotypeKernel learningLearning applicationsGeneralization powerAccurate patient stratificationSample classificationLearning approachData typesBlack-boxLearning tasksDeterministic domainsAutomatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis
Lee K, Khoo HM, Fourcade C, Gotman J, Grova C. Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Magnetic Resonance Imaging 2019, 58: 97-107. PMID: 30695721, DOI: 10.1016/j.mri.2019.01.019.Peer-Reviewed Original ResearchConceptsReal dataNumber of atomsSubject-specific thresholdFunctional connectivity MRI analysisState networksAutomatic removal methodSpatial priorsSet of voxelsBootstrap resamplingSparse dictionary learningStepwise regression procedureNoiseHub analysisRegression procedureInter-network communicationNew methodAtomsBand-pass filteringTemporal correlationFluctuationsPriorsSparsityDictionary learningNetworkWhole-brain signals
2018
Data science and artificial intelligence to improve clinical practice and research
Ohno-Machado L. Data science and artificial intelligence to improve clinical practice and research. Journal Of The American Medical Informatics Association 2018, 25: 1273-1273. PMID: 30312446, PMCID: PMC7646927, DOI: 10.1093/jamia/ocy136.Commentaries, Editorials and LettersIdentifying and characterizing highly similar notes in big clinical note datasets
Gabriel R, Kuo T, McAuley J, Hsu C. Identifying and characterizing highly similar notes in big clinical note datasets. Journal Of Biomedical Informatics 2018, 82: 63-69. PMID: 29679685, DOI: 10.1016/j.jbi.2018.04.009.Peer-Reviewed Original ResearchConceptsClinical note datasetsDe-duplication algorithmMIMIC-III datasetElectronic health recordsJaccard similarityDe-duplicationLocality Sensitive HashingMIMIC-IIINear-duplicatesScalable algorithmMeasure similarityAccurate statistical modelsSources of duplicationClustering methodDatasetAlgorithmApproximation algorithmHealth recordsDisjoint setsInstitutional datasetComparison of notesPairs of notesHashPairwise comparisonsPairwise
2017
Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention
Onofrey JA, Staib LH, Sarkar S, Venkataraman R, Nawaf CB, Sprenkle PC, Papademetris X. Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention. Medical Image Analysis 2017, 39: 29-43. PMID: 28431275, PMCID: PMC5514316, DOI: 10.1016/j.media.2017.04.001.Peer-Reviewed Original Research
2016
A comprehensive tractography study of patients with bipolar disorder and their unaffected siblings
Sprooten E, Barrett J, McKay DR, Knowles EE, Mathias SR, Winkler AM, Brumbaugh MS, Landau S, Cyr L, Kochunov P, Glahn DC. A comprehensive tractography study of patients with bipolar disorder and their unaffected siblings. Human Brain Mapping 2016, 37: 3474-3485. PMID: 27198848, PMCID: PMC5496097, DOI: 10.1002/hbm.23253.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlgorithmsBipolar DisorderBrainComorbidityDiffusion Magnetic Resonance ImagingDiffusion Tensor ImagingFemaleGenetic Predisposition to DiseaseHumansImage Interpretation, Computer-AssistedMaleNeural PathwaysOrgan SizePattern Recognition, AutomatedPsychiatric Status Rating ScalesSeverity of Illness IndexSiblingsYoung AdultChemical named entity recognition in patents by domain knowledge and unsupervised feature learning
Zhang Y, Xu J, Chen H, Wang J, Wu Y, Prakasam M, Xu H. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning. Database 2016, 2016: baw049. PMID: 27087307, PMCID: PMC4834204, DOI: 10.1093/database/baw049.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputational BiologyData MiningDatabases, ChemicalPatents as TopicPattern Recognition, AutomatedPharmaceutical PreparationsUnsupervised Machine LearningConceptsMachine learning-based systemsLearning-based systemConditional Random FieldsDomain knowledgeEntity recognitionMatthews correlation coefficientDrug Named Entity RecognitionBioCreative V challengeInformation extraction systemWord representation featuresUnsupervised feature learningUnsupervised learning algorithmNamed Entity RecognitionSemantic type informationSupport vector machinePrecision-recall curveBrown clusteringFeature learningFeature engineeringUnsupervised featureIndividual subtasksMining systemNER taskLearning algorithmCPD task
2015
“Omics” of High Altitude Biology: A Urinary Metabolomics Biomarker Study of Rats Under Hypobaric Hypoxia
Koundal S, Gandhi S, Kaur T, Mazumder A, Khushu S. “Omics” of High Altitude Biology: A Urinary Metabolomics Biomarker Study of Rats Under Hypobaric Hypoxia. OMICS A Journal Of Integrative Biology 2015, 19: 757-765. PMID: 26669710, DOI: 10.1089/omi.2015.0155.Peer-Reviewed Original ResearchConceptsHypobaric hypoxiaSports medicineHypobaric hypoxia exposurePreclinical rat modelHigh altitude medicineAltitude medicineCell membrane metabolismUse of metabolomicsUrinary metabolomeHepatic functionLiver histopathologyRat modelLiver functioningHypoxia exposureHealth problemsMultivariate analysisPotential biomarkersAttendant health problemsTCA cycle metabolitesBiomarker studiesHypoxiaUrine samplesTaurine metabolismMembrane metabolismCycle metabolitesRecognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods.
Tang B, Chen Q, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H. Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods. AMIA Annual Symposium Proceedings 2015, 2015: 1184-93. PMID: 26958258, PMCID: PMC4765674.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansMachine LearningNatural Language ProcessingPattern Recognition, AutomatedSemanticsA Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text.
Wu Y, Xu J, Jiang M, Zhang Y, Xu H. A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text. AMIA Annual Symposium Proceedings 2015, 2015: 1326-33. PMID: 26958273, PMCID: PMC4765694.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsData CurationHumansNatural Language ProcessingPattern Recognition, AutomatedSemanticsTerminology as TopicConceptsNamed Entity RecognitionClinical NER systemNeural word embeddingsClinical Named Entity RecognitionWord embeddingsNER systemWord representationsI2b2 dataEntity recognitionEmbedding featuresClinical textNatural language processing researchConditional Random FieldsLanguage processing researchWord embedding featuresLarge unlabeled corpusBrown clustersNeural wordImportant patient informationFeature representationF1 scoreIntelligent monitoringCritical taskUnlabeled corpusSemantic relationsPrimary and secondary alterations of white matter connectivity in schizophrenia: A study on first-episode and chronic patients using whole-brain tractography-based analysis
Wu C, Hwang T, Chen Y, Hsu Y, Lo Y, Liu C, Hwu H, Liu C, Hsieh M, Chien Y, Chen C, Tseng W. Primary and secondary alterations of white matter connectivity in schizophrenia: A study on first-episode and chronic patients using whole-brain tractography-based analysis. Schizophrenia Research 2015, 169: 54-61. PMID: 26443482, DOI: 10.1016/j.schres.2015.09.023.Peer-Reviewed Original ResearchConceptsDorsolateral prefrontal cortexBilateral DLPFCFirst-episode patientsWhite matter connectivityFirst-episodeChronic patientsBilateral dorsolateral prefrontal cortexFirst-episode schizophreniaWidespread white matter abnormalitiesBilateral temporal polesDebilitating mental disorderReduced white matter connectivityRight arcuate fasciculusSuperior longitudinal fasciculus IDuration of illnessAssociated with clinical variablesCallosal fibersBetween-group analysisWhite matter tractsWhite matter abnormalitiesPrefrontal cortexImpaired connectivityTemporal poleArcuate fasciculusCerebral white matterIdentifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2
Stubbs A, Kotfila C, Xu H, Uzuner Ö. Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. Journal Of Biomedical Informatics 2015, 58: s67-s77. PMID: 26210362, PMCID: PMC4978189, DOI: 10.1016/j.jbi.2015.07.001.Peer-Reviewed Original ResearchMeSH KeywordsAgedBostonCohort StudiesComorbidityComputer SecurityConfidentialityCoronary Artery DiseaseData MiningDiabetes ComplicationsElectronic Health RecordsFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedNarrationNatural Language ProcessingPattern Recognition, AutomatedRisk AssessmentVocabulary, ControlledConceptsCoronary artery diseaseRisk factorsLongitudinal medical recordsMedical recordsMedical risk factorsArtery diseaseDiabetic patientsSmoking statusHeart diseaseFamily historyI2b2/UTHealth natural language processingDiseaseI2b2/UTHealthProgressionUTHealthHypertensionHyperlipidemiaFactorsObesityDiabetesPatientsEase of adoption of clinical natural language processing software: An evaluation of five systems
Zheng K, Vydiswaran V, Liu Y, Wang Y, Stubbs A, Uzuner Ö, Gururaj A, Bayer S, Aberdeen J, Rumshisky A, Pakhomov S, Liu H, Xu H. Ease of adoption of clinical natural language processing software: An evaluation of five systems. Journal Of Biomedical Informatics 2015, 58: s189-s196. PMID: 26210361, PMCID: PMC4974203, DOI: 10.1016/j.jbi.2015.07.008.Peer-Reviewed Original ResearchMeSH KeywordsAttitude to ComputersData MiningElectronic Health RecordsHumansMiddle AgedNatural Language ProcessingPattern Recognition, AutomatedSoftwareUser-Computer InterfaceConceptsClinical NLP systemsNLP systemsNatural language processing softwareThird-party componentsUsability testing toolGroup of usersLanguage processing softwareEase of adoptionExpert evaluatorsSoftware distributionBiomedical softwareComputer scienceEnd usersUsability assessmentI2b2 challengeTesting toolsEvaluation showHuman evaluatorsSystem submissionsEase of useHealth informaticsProcessing softwareAdoption issuesUsersSpecial trackSegmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration
Onofrey JA, Staib LH, Papademetris X. Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration. Lecture Notes In Computer Science 2015, 24: 662-674. PMID: 26221711, PMCID: PMC5266617, DOI: 10.1007/978-3-319-19992-4_52.Peer-Reviewed Original Research
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