2013
Differential Expression of miR-145 in Children with Kawasaki Disease
Shimizu C, Kim J, Stepanowsky P, Trinh C, Lau H, Akers J, Chen C, Kanegaye J, Tremoulet A, Ohno-Machado L, Burns J. Differential Expression of miR-145 in Children with Kawasaki Disease. PLOS ONE 2013, 8: e58159. PMID: 23483985, PMCID: PMC3590129, DOI: 10.1371/journal.pone.0058159.Peer-Reviewed Original ResearchMeSH KeywordsArteriesBase SequenceChildChild, PreschoolCluster AnalysisGene Expression RegulationHumansInfantMicroRNAsModels, BiologicalMolecular Sequence DataMucocutaneous Lymph Node SyndromeReal-Time Polymerase Chain ReactionSequence AlignmentSequence Analysis, DNASignal TransductionTransforming Growth Factor betaConceptsTGF-β pathwayGene expressionMiR-145Small non-coding RNAsKawasaki disease pathogenesisExtracellular vesiclesSmall RNA speciesPost-transcriptional levelDiscovery of microRNAsKawasaki diseaseNon-coding RNAsExpression of genesDisease pathogenesisSmall extracellular vesiclesSmall RNAsRNA speciesTarget genesTop pathwaysVascular smooth muscle cellsPathway analysisDifferentiation of neutrophilsDifferential expressionMicroRNAsArterial wallGeneration of myofibroblastsComparison of four prediction models to discriminate benign from malignant vertebral compression fractures according to MRI feature analysis.
Thawait S, Kim J, Klufas R, Morrison W, Flanders A, Carrino J, Ohno-Machado L. Comparison of four prediction models to discriminate benign from malignant vertebral compression fractures according to MRI feature analysis. American Journal Of Roentgenology 2013, 200: 493-502. PMID: 23436836, DOI: 10.2214/ajr.11.7192.Peer-Reviewed Original ResearchAdultAgedAged, 80 and overAlgorithmsCohort StudiesComputer SimulationDiagnosis, DifferentialFemaleFractures, CompressionHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedModels, BiologicalNeoplasmsPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySpinal FracturesYoung Adult
2012
Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): An Algorithm for Joint Optimization of Discrimination and Calibration
Jiang X, Menon A, Wang S, Kim J, Ohno-Machado L. Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): An Algorithm for Joint Optimization of Discrimination and Calibration. PLOS ONE 2012, 7: e48823. PMID: 23139819, PMCID: PMC3490990, DOI: 10.1371/journal.pone.0048823.Peer-Reviewed Original Research
2007
MODELING CANCER: INTEGRATION OF "OMICS" INFORMATION IN DYNAMIC SYSTEMS
STRANSKY B, BARRERA J, OHNO-MACHADO L, DE SOUZA S. MODELING CANCER: INTEGRATION OF "OMICS" INFORMATION IN DYNAMIC SYSTEMS. Journal Of Bioinformatics And Computational Biology 2007, 5: 977-986. PMID: 17787066, DOI: 10.1142/s0219720007002990.Peer-Reviewed Original Research
2005
The use of receiver operating characteristic curves in biomedical informatics
Lasko T, Bhagwat J, Zou K, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. Journal Of Biomedical Informatics 2005, 38: 404-415. PMID: 16198999, DOI: 10.1016/j.jbi.2005.02.008.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCombining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation
Haker S, Wells W, Warfield S, Talos I, Bhagwat J, Goldberg-Zimring D, Mian A, Ohno-Machado L, Zou K. Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation. Lecture Notes In Computer Science 2005, 8: 506-514. PMID: 16685884, PMCID: PMC3681096, DOI: 10.1007/11566465_63.Peer-Reviewed Original Research
2004
Research on machine learning issues in biomedical informatics modeling
Ohno-Machado L. Research on machine learning issues in biomedical informatics modeling. Journal Of Biomedical Informatics 2004, 37: 221-223. PMID: 15465475, DOI: 10.1016/j.jbi.2004.07.004.Commentaries, Editorials and LettersA primer on gene expression and microarrays for machine learning researchers
Kuo W, Kim E, Trimarchi J, Jenssen T, Vinterbo S, Ohno-Machado L. A primer on gene expression and microarrays for machine learning researchers. Journal Of Biomedical Informatics 2004, 37: 293-303. PMID: 15465482, DOI: 10.1016/j.jbi.2004.07.002.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsNew algorithmSupervised learning modelUCI machineLearning modelMicroarray data analysisAlgorithmic developmentsTypes of dataMachineData setsMain challengesGene expression dataMain motivationAlgorithmData analysisBiomedical experimentsLarge numberExpression dataMicroarray dataResearchersRepositoryWebMicroarray experimentsNew waveDataSetA greedy algorithm for supervised discretization
Butterworth R, Simovici D, Santos G, Ohno-Machado L. A greedy algorithm for supervised discretization. Journal Of Biomedical Informatics 2004, 37: 285-292. PMID: 15465481, DOI: 10.1016/j.jbi.2004.07.006.Peer-Reviewed Original Research
1999
A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction.
Vinterbo S, Ohno-Machado L. A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction. AMIA Annual Symposium Proceedings 1999, 984-8. PMID: 10566508, PMCID: PMC2232877.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsChest PainEvaluation Studies as TopicGeneticsHumansLogistic ModelsModels, BiologicalMyocardial InfarctionROC CurveConceptsGenetic algorithmNumber of variablesVariable selection methodsGenetic algorithm variable selection methodSelection methodData setsAlgorithmVariable selectionBest variable combinationModel's discriminatory performanceModel simplicityActual useValidation setExternal validation setSetParticular selectionModel