Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia
Xing Y, Pearlson G, Kochunov P, Calhoun V, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. NeuroImage 2024, 299: 120839. PMID: 39251116, PMCID: PMC11491165, DOI: 10.1016/j.neuroimage.2024.120839.Peer-Reviewed Original ResearchConceptsSelection methodClassification accuracy gainsGraph-based regularizationHigh-dimensional dataFeature selection methodLocal structural informationSparse regularizationAblation studiesFeature subsetPublic datasetsFeature selectionClassification accuracyExperimental evaluationAccuracy gainsSelection techniquesNetwork connectivityData transformationSuperior performanceDatasetConvergence analysisStructural informationClassificationRegularizationFeaturesDisorder predictionNeuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade
Du Y, Niu J, Xing Y, Li B, Calhoun V. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophrenia Bulletin 2024, sbae110. PMID: 38982882, DOI: 10.1093/schbul/sbae110.Peer-Reviewed Original ResearchArtificial intelligenceSemi-supervised learning methodArtificial intelligence techniquesAccurate diagnosis of SZMultimodal fusionAccurate diagnosis of schizophreniaIntelligence techniquesAI methodsLearning methodsDiagnosis of SZMental disordersSelection methodUnsupervised clusteringMagnetic resonance imagingBiomarker extractionDiagnosis of schizophrenia