2011
AnyExpress: Integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm
Kim J, Patel K, Jung H, Kuo W, Ohno-Machado L. AnyExpress: Integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm. BMC Bioinformatics 2011, 12: 75. PMID: 21410990, PMCID: PMC3076267, DOI: 10.1186/1471-2105-12-75.Peer-Reviewed Original Research
2010
DSGeo: Software tools for cross-platform analysis of gene expression data in GEO
Lacson R, Pitzer E, Kim J, Galante P, Hinske C, Ohno-Machado L. DSGeo: Software tools for cross-platform analysis of gene expression data in GEO. Journal Of Biomedical Informatics 2010, 43: 709-715. PMID: 20435161, PMCID: PMC2934864, DOI: 10.1016/j.jbi.2010.04.007.Peer-Reviewed Original ResearchConceptsAggregation of dataData loaderRelational databaseGene expression dataUser preferencesData browserData browsingCross-platform dataSoftware toolsSeamless integrationCross-platform analysisGroups of dataQueriesExpression dataPublic gene expression dataSpecific sample characteristicsLarge resourcesBrowserToolBrowsingAnnotatingUsersRetrievalApplicationsPlatform
2005
Small, fuzzy and interpretable gene expression based classifiers
Vinterbo S, Kim E, Ohno-Machado L. Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 2005, 21: 1964-1970. PMID: 15661797, DOI: 10.1093/bioinformatics/bti287.Peer-Reviewed Original ResearchConceptsRule-based classifierBiomedical domainClassification modelFuzzy logicClassifierBiomedical researchersGene expression dataDataset
2004
A 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 waveDataSetThe Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer
Jaroszewicz S, Simovici D, Kuo W, Ohno-Machado L. The Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer. IEEE Transactions On Biomedical Engineering 2004, 51: 1095-1102. PMID: 15248526, DOI: 10.1109/tbme.2004.827267.Peer-Reviewed Original ResearchMultivariate selection of genetic markers in diagnostic classification
Weber G, Vinterbo S, Ohno-Machado L. Multivariate selection of genetic markers in diagnostic classification. Artificial Intelligence In Medicine 2004, 31: 155-167. PMID: 15219292, DOI: 10.1016/j.artmed.2004.01.011.Peer-Reviewed Original ResearchConceptsClassification performanceBetter classification performanceLogistic regression algorithmUser-friendly implementationDifferent data setsSophisticated algorithmsRegression algorithmAlgorithmNew algorithmParticular classificationUnivariate algorithmsData setsGene expression dataClassificationNumber of variablesGene selectionSetInternetExpression dataNew setViable choiceMachinePerformanceImplementationSelection
2003
Stochastic Algorithms for Gene Expression Analysis
Ohno-Machado L, Kuo W. Stochastic Algorithms for Gene Expression Analysis. Lecture Notes In Computer Science 2003, 2827: 39-49. DOI: 10.1007/978-3-540-39816-5_4.Peer-Reviewed Original Research
2002
Visualization and evaluation of clusters for exploratory analysis of gene expression data
Kim J, Kohane I, Ohno-Machado L. Visualization and evaluation of clusters for exploratory analysis of gene expression data. Journal Of Biomedical Informatics 2002, 35: 25-36. PMID: 12415724, DOI: 10.1016/s1532-0464(02)00001-1.Peer-Reviewed Original ResearchConceptsClustering algorithmDifferent clustering algorithmsPopular clustering algorithmNew clustering algorithmComprehensive data visualizationGene expression data analysisData visualization strategiesExpression data analysisEvaluation of clustersData visualizationSoftware toolsCluster qualityCluster consistencyAlgorithmActual implementationData setsGene expression dataQuality measuresVisualizationPromising resultsFrameworkData analysisObjective evaluationUsersExpression dataBuilding an asynchronous web-based tool for machine learning classification.
Weber G, Vinterbo S, Ohno-Machado L. Building an asynchronous web-based tool for machine learning classification. AMIA Annual Symposium Proceedings 2002, 869-73. PMID: 12463949, PMCID: PMC2244467.Peer-Reviewed Original ResearchConceptsClassification methodDownloadable software toolsWeb-based applicationSupervised learning methodsSupport vector machineNearest neighbor classifierWeb-based toolWeb-based analysis toolWeb applicationBioinformatics LaboratorySoftware toolsFree softwareLearning methodsSophisticated algorithmsVector machineNeighbor classifierLinear discriminant analysisAnalysis toolsDisease classificationClassification treesLimited budgetShort amountRemote locationsHigh-throughput gene expression dataGene expression dataGene expression levels in different stages of progression in oral squamous cell carcinoma.
Kuo W, Jenssen T, Park P, Lingen M, Hasina R, Ohno-Machado L. Gene expression levels in different stages of progression in oral squamous cell carcinoma. AMIA Annual Symposium Proceedings 2002, 415-9. PMID: 12474876, PMCID: PMC2244435.Peer-Reviewed Original ResearchConceptsOral squamous cell carcinomaExpression levelsGene expression studiesGene expression dataGene expression levelsChromosome domainsCancer samplesExpression studiesSquamous cell carcinomaMolecular mechanismsExpression dataGenesMolecular levelProgression of OSCCCell carcinomaGenetic featuresCancer typesCommon cancer typesImportant insightsSignificant differencesSmall panelPatient samplesDifferent stagesProgressionSample types