2025
Broken time-reversal symmetry in visual motion detection
Wu N, Zhou B, Agrochao M, Clark D. Broken time-reversal symmetry in visual motion detection. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2410768122. PMID: 40048271, PMCID: PMC11912477, DOI: 10.1073/pnas.2410768122.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsDrosophila melanogasterMotion PerceptionNeural Networks, ComputerPhotic StimulationConceptsNeural network modelTraining dataNetwork modelTrained neural network modelFlexible neural networkBiological motion detectorsMotion estimationTime-reversal symmetryNeural networkMotion detectionVisual motion detectionMotion detectorsVisual systemSymmetry breakingTime-reversal symmetry breakingContrast distributionPerception of motionReversal symmetryTrainingMovieMotion perceptionSceneTime reversalIntuitionNetworkT‑ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment
Kyro G, Smaldone A, Shee Y, Xu C, Batista V. T‑ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment. Journal Of Chemical Information And Modeling 2025, 65: 2395-2415. PMID: 39965912, DOI: 10.1021/acs.jcim.4c02332.Peer-Reviewed Original ResearchMeSH KeywordsCoronavirus 3C ProteasesDeep LearningDrug DiscoveryHumansLigandsNeural Networks, ComputerProtein BindingProteinsSARS-CoV-2UncertaintyConceptsProtein-ligand binding affinity predictionBinding affinity predictionState-of-the-art performanceTransformer-based deep neural networksMultimodal feature representationAffinity predictionBinding affinity of small moleculesState-of-the-artDeep neural networksDeep learning modelsAffinity of small moleculesSelf-learning methodSARS-CoV-2 main proteasePredicted binding affinitiesFeature representationBinding affinityOn-target potencyNeural networkDrug discovery applicationsTransformation frameworkLearning modelsScoring functionCrystal structureSelf-learningMain proteaseArtificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study
Oikonomou E, Vaid A, Holste G, Coppi A, McNamara R, Baloescu C, Krumholz H, Wang Z, Apakama D, Nadkarni G, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. The Lancet Digital Health 2025, 7: e113-e123. PMID: 39890242, DOI: 10.1016/s2589-7500(24)00249-8.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemPoint-of-care ultrasonographyMount Sinai Health SystemTransthyretin amyloid cardiomyopathyArtificial intelligenceHealth systemAmyloid cardiomyopathyHypertrophic cardiomyopathyRetrospective cohort of individualsCardiomyopathy casesTesting artificial intelligenceConvolutional neural networkSinai Health SystemCohort of individualsOpportunistic screeningHypertrophic cardiomyopathy casesMulti-labelPositive screenAI frameworkEmergency departmentMortality riskNeural networkLoss functionCardiac ultrasonographyAugmentation approachDifferential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females
Heyn S, Keding T, Cisler J, McLaughlin K, Herringa R. Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females. Scientific Reports 2025, 15: 651. PMID: 39753729, PMCID: PMC11698963, DOI: 10.1038/s41598-024-84616-5.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentChild AbuseCohort StudiesFemaleGray MatterHumansMachine LearningModels, PsychologicalNeural Networks, ComputerPsychopathologyConceptsGray matter volumeVoxel-based morphometryInternalizing psychopathologyChildhood abuseAbuse experiencesPrefrontal cortexCingulate cortexAssociated with increased GMVVoxel-based morphometry analysisInterpersonal violenceDorsal prefrontal cortexDevelopment of psychopathologyAnterior cingulate cortexChildhood abuse historyChildhood abuse exposureT1 structural MRIDifferentiating gray matterPredictive of abuseAdolescent femalesSeverity of abuseDegree of overlapSupramarginal gyrusTrauma exposureStudy of interpersonal violenceIndividual psychopathologyDeep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy
Guo M, Wu Y, Hobson C, Su Y, Qian S, Krueger E, Christensen R, Kroeschell G, Bui J, Chaw M, Zhang L, Liu J, Hou X, Han X, Lu Z, Ma X, Zhovmer A, Combs C, Moyle M, Yemini E, Liu H, Liu Z, Benedetto A, La Riviere P, Colón-Ramos D, Shroff H. Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Nature Communications 2025, 16: 313. PMID: 39747824, PMCID: PMC11697233, DOI: 10.1038/s41467-024-55267-x.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCaenorhabditis elegansDeep LearningImage Processing, Computer-AssistedMiceMicroscopy, ConfocalMicroscopy, FluorescenceNeural Networks, ComputerConceptsAdaptive optics techniquesMulti-photonDeep learning-based strategyAberration compensationLearning-based strategyTrained neural networkImprove image qualityOptical aberrationsNeural networkImage quantitationOptical techniquesDiverse datasetsSuper-resolution microscopyLight sheetRestore dataImage qualityImage signalNetworkImage inspectionImage acquisitionImage stacksOpticsResolutionImagesFluorescence microscopyNeural network analysis as a novel skin outcome in a trial of belumosudil in patients with systemic sclerosis
Gunes I, Bernstein E, Cowper S, Panse G, Pradhan N, Camacho L, Page N, Bundschuh E, Williams A, Carns M, Aren K, Fantus S, Volkmann E, Bukiri H, Correia C, Kolachalama V, Wilson F, Mawe S, Mahoney J, Hinchcliff M. Neural network analysis as a novel skin outcome in a trial of belumosudil in patients with systemic sclerosis. Arthritis Research & Therapy 2025, 27: 85. PMID: 40217251, DOI: 10.1186/s13075-025-03508-9.Peer-Reviewed Original ResearchMeSH KeywordsAdultBiopsyFemaleHumansMaleMiddle AgedNeural Networks, Computerrho-Associated KinasesScleroderma, SystemicSkinTreatment OutcomeConceptsModified Rodnan skin scoreSystemic sclerosisFibrosis scoreHistological parametersDiffuse cutaneous systemic sclerosisInterquartile rangeSystemic sclerosis skin biopsiesRodnan skin scoreCutaneous systemic sclerosisOpen-label trialSkin scorePathological parametersSkin biopsiesBiopsySkin outcomesStudy terminationFibrosisHistological analysisBlinded dermatopathologistStained sectionsSpearman correlationBelumosudilPatientsSkin featuresScores
2024
Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis
Sharifi G, Hajibeygi R, Zamani S, Easa A, Bahrami A, Eshraghi R, Moafi M, Ebrahimi M, Fathi M, Mirjafari A, Chan J, Dixe de Oliveira Santo I, Anar M, Rezaei O, Tu L. Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis. Emergency Radiology 2024, 32: 97-111. PMID: 39680295, DOI: 10.1007/s10140-024-02300-7.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansNeural Networks, ComputerRadiographic Image Interpretation, Computer-AssistedSensitivity and SpecificitySkull FracturesTomography, X-Ray ComputedConceptsConvolutional neural networkArea under the receiver operating characteristic curveConvolutional neural network modelCT scanSkull fractureComputed tomographyDeep learningProspective clinical trialMeta-analysisReceiver operating characteristic curvePublication biasSkull fracture detectionSystematic reviewNeural network algorithmDetecting skull fracturesImprove diagnosis accuracyDiagnostic hurdlesShortage of radiologistsAutomated diagnostic toolTransfer learningDiagnostic performanceDiagnostic accuracyClinical trialsModel architectureNeural networkBrain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 39708510, PMCID: PMC11877132, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBrainFemaleHumansIntelligenceMagnetic Resonance ImagingMaleNerve NetNeural Networks, ComputerRestConceptsGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesArchitectureDeep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability
Zhou X, Kedia S, Meng R, Gerstein M. Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability. PLOS ONE 2024, 19: e0312848. PMID: 39630834, PMCID: PMC11616848, DOI: 10.1371/journal.pone.0312848.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAlzheimer DiseaseBrainDeep LearningFemaleHumansMagnetic Resonance ImagingMaleNeural Networks, ComputerNeuroimagingConceptsConvolutional neural networkNeural networkAlzheimer's diseaseConvolutional neural network modelMultimodal medical datasetsDeep learning methodsPotential of deep learningGenetic risk factorsMedical datasetsAlzheimer's Disease Neuroimaging InitiativeAD predictionDeep learningDeep learning analysisLearning methodsMedical imagesPredicting Alzheimer's diseaseDetection of Alzheimer's diseaseModel interpretationEarly detection of Alzheimer's diseaseAccuracy levelGenetic factorsDatasetEarly detection of ADNetworkDetection of ADImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchMeSH KeywordsAdultAttentionBrainDeep LearningHumansMachine LearningMagnetic Resonance ImagingMultimodal ImagingNeural Networks, ComputerNeuroimagingSchizophreniaConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsMachine-guided design of cell-type-targeting cis-regulatory elements
Gosai S, Castro R, Fuentes N, Butts J, Mouri K, Alasoadura M, Kales S, Nguyen T, Noche R, Rao A, Joy M, Sabeti P, Reilly S, Tewhey R. Machine-guided design of cell-type-targeting cis-regulatory elements. Nature 2024, 634: 1211-1220. PMID: 39443793, PMCID: PMC11525185, DOI: 10.1038/s41586-024-08070-z.Peer-Reviewed Original ResearchConceptsCis-regulatory elementsCell typesActivation of off-target cellsGene expressionCell type-specific expressionSynthetic cis-regulatory elementsCell-type specificityHuman genomeUnique cell typeTissue identityBiotechnological applicationsTissue specificityIn vitro validationCell linesCre activitySequenceGenesNatural sequenceDevelopmental timeExpressionCellsGenomeTested in vivoMotifOff-target cellsA neural network model of differentiation and integration of competing memories
Ritvo V, Nguyen A, Turk-Browne N, Norman K. A neural network model of differentiation and integration of competing memories. ELife 2024, 12: rp88608. PMID: 39319791, PMCID: PMC11424095, DOI: 10.7554/elife.88608.Peer-Reviewed Original ResearchConceptsNeural network modelUnsupervised neural network modelUnsupervised learning mechanismLearning mechanismLearning modelsNetwork modelComputational explanationInactive memoryNeural representationActive competitorsDiverse setRepresentationMemoryRepresentation of memoryMemory literatureBrain regionsNovel predictionsPredicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Song T, Cosatto E, Wang G, Kuang R, Gerstein M, Min M, Warrell J. Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs. Bioinformatics 2024, 40: ii111-ii119. PMID: 39230702, PMCID: PMC11373608, DOI: 10.1093/bioinformatics/btae383.Peer-Reviewed Original ResearchMeSH KeywordsBreast NeoplasmsFemaleGene Expression ProfilingHumansNeural Networks, ComputerTranscriptomeConceptsGene expressionSpatial gene expressionSpatial transcriptomics technologiesTissue histology imagesExpressed genesGene activationTranscriptomic technologiesMolecular underpinningsGraph neural networksState-of-the-artSpatial expressionGenesTissue architectureExpressionHistological imagesNeural networkCGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection
Li H, Han Z, Sun Y, Wang F, Hu P, Gao Y, Bai X, Peng S, Ren C, Xu X, Liu Z, Chen H, Yang Y, Bo X. CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection. Nature Communications 2024, 15: 5997. PMID: 39013885, PMCID: PMC11252405, DOI: 10.1038/s41467-024-50426-6.Peer-Reviewed Original ResearchCross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers
Hassanzadeh R, Abrol A, Hassanzadeh H, Calhoun V. Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039975, DOI: 10.1109/embc53108.2024.10781737.Peer-Reviewed Original ResearchMeSH KeywordsAgedAlzheimer DiseaseBiomarkersBrainFemaleHumansMagnetic Resonance ImagingMaleNeural Networks, ComputerConceptsFunctional network connectivityGenerative adversarial networkStructural similarity index measureT1-weighted structural magnetic resonance imaging dataAdversarial networkStructural magnetic resonance imaging dataIncreased functional connectivityMagnetic resonance imaging dataSimilarity index measureCross-modal transformerCross-modal translationPatterns of atrophyAlzheimer's diseaseFunctional connectivityReduced connectivityMotor-visualTemporal regionsWeak supervisionAlzheimer's disease biomarkersControl networkCycle-GANCross-modalAlzheimer patientsContext of Alzheimer's diseaseGeneration approachA Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification
Zhao M, Xu R, Zhi D, Yu S, Calhoun V, Sui J. A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40038938, DOI: 10.1109/embc53108.2024.10781810.Peer-Reviewed Original ResearchMeSH KeywordsBrainBrain DiseasesHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingNeural Networks, ComputerSchizophreniaConceptsLearning frameworkMutual learning frameworkEnd-to-endDeep learning approachMutual knowledge transferEnsemble decisionClassification performanceCross featuresJoint lossLearning approachNetwork connectivityKnowledge transferEncodingAdaptive integrationIndependent componentsCollaborative learningDynamic dependenceTC-specificRobust characteristicsLearningStudy of brain disordersDisorder classificationEmpirical resultsCross-modal modulationAccuracyCGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal
Cui X, Zhi D, Yan W, Calhoun V, Zhuo C, Sui J. CGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039732, DOI: 10.1109/embc53108.2024.10782176.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBrainDatabases, FactualHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingNeural Networks, ComputerNeuroimagingSupervised Machine LearningConceptsSelf-supervised learningIntrinsic image propertiesGeneralization of modelsSynthetic datasetsClassification performanceGenerative modelDiscrepancy minimizationImage dataNetwork approachDatasetData harmonizationImaging propertiesLearningNeuroimaging classificationCycleGANData harmonization methodsAdversaryABCD datasetAcquisition protocolsPerformanceEffective wayDataTaskSpach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising
Jang S, Pan T, Li Y, Heidari P, Chen J, Li Q, Gong K. Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising. IEEE Transactions On Medical Imaging 2024, 43: 2036-2049. PMID: 37995174, PMCID: PMC11111593, DOI: 10.1109/tmi.2023.3336237.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansImage Processing, Computer-AssistedImaging, Three-DimensionalNeural Networks, ComputerPositron-Emission TomographySignal-To-Noise RatioConceptsMulti-head self-attentionConvolutional neural networkSelf-attentionSignal-to-noise ratioState-of-the-art deep learning architecturesGlobal self-attentionState-of-the-artLocal feature extractionDeep learning architectureLow signal-to-noise ratioImage denoisingChannel informationChannel-wiseLearning architectureFeature extractionNeural networkTransformation frameworkComputational costReceptive fieldsImage qualityQuantitative meritDenoisingFrameworkQuantitative resultsDatasetComputational reconstruction of mental representations using human behavior
Caplette L, Turk-Browne N. Computational reconstruction of mental representations using human behavior. Nature Communications 2024, 15: 4183. PMID: 38760341, PMCID: PMC11101448, DOI: 10.1038/s41467-024-48114-6.Peer-Reviewed Original ResearchMeSH KeywordsAdultBehaviorCognitionFemaleHumansMaleNeural Networks, ComputerPhotic StimulationSemanticsVisual PerceptionYoung AdultConceptsMental representationsGoal of cognitive scienceVisual featuresNeural networkMultiple visual conceptsDeep neural networksConceptual representationCognitive scienceVisual conceptsSemantic spaceSemantic featuresHuman behaviorParticipantsNetworkRepresentationStimuliBehaviorFeaturesImagesComputer reconstructionTaskBERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks
Pelletier S, Leclercq M, Roux-Dalvai F, de Geus M, Leslie S, Wang W, Lam T, Nairn A, Arnold S, Carlyle B, Precioso F, Droit A. BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks. Nature Communications 2024, 15: 3777. PMID: 38710683, PMCID: PMC11074280, DOI: 10.1038/s41467-024-48177-5.Peer-Reviewed Original ResearchMeSH KeywordsChromatography, LiquidHumansMass SpectrometryNeural Networks, ComputerReproducibility of ResultsConceptsLC-MS experimentsLC-MSLiquid chromatography mass spectrometry dataComplex biological samplesMass spectrometry dataLiquid chromatography mass spectrometryChromatography mass spectrometryMass spectrometrySpectrometry dataEffective removalBiological samplesExperimental conditionsBatch effect removalSample processing protocolBatch effectsSpectrometryBatch effect correction methodsCorrecting batch effectsRemoval of batch effects
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply