2025
scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Wang G, Zhao J, Lin Y, Liu T, Zhao Y, Zhao H. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nature Communications 2025, 16: 4994. PMID: 40442129, PMCID: PMC12122792, DOI: 10.1038/s41467-025-60333-z.Peer-Reviewed Original ResearchConceptsDeep learning frameworkSingle-cell multi-omics researchSingle-cell multi-omics dataLearning frameworkMulti-omics dataGenerative adversarial networkSingle-cell technologiesData alignmentSingle-cell resolutionMulti-omics researchDownstream analysisCellular statesOmics datasetsAdversarial networkNeural networkProteomic profilingCorrelated featuresBiological informationOmics perspectiveDiverse datasetsFeature topologyDisease mechanismsCell embeddingData resourcesRelationship inference
2024
ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis
Xu H, Hu R, Dong X, Kuang L, Zhang W, Tu C, Li Z, Zhao Z. ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis. Nature Communications 2024, 15: 8926. PMID: 39414796, PMCID: PMC11484853, DOI: 10.1038/s41467-024-53296-0.Peer-Reviewed Original ResearchCUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Liu C, Amodio M, Shen L, Gao F, Avesta A, Aneja S, Wang J, Del Priore L, Krishnaswamy S. CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation. Lecture Notes In Computer Science 2024, 15008: 155-165. DOI: 10.1007/978-3-031-72111-3_15.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationLack of labeled dataUnsupervised deep learning frameworkSegmenting medical imagesDeep learning frameworkBrain MRI imagesRetinal fundus imagesContrastive learningLearning frameworkUnsupervised methodDeep learningExpert annotationsData topologyMedical imagesGranularity levelsEmbedding mapHausdorff distanceFundus imagesDice coefficientImage dataEmbeddingAnnotationLearningMRI imagesTricuspid valve flow measurement using a deep learning framework for automated valve‐tracking 2D phase contrast
Lamy J, Gonzales R, Xiang J, Seemann F, Huber S, Steele J, Wieben O, Heiberg E, Peters D. Tricuspid valve flow measurement using a deep learning framework for automated valve‐tracking 2D phase contrast. Magnetic Resonance In Medicine 2024, 92: 1838-1850. PMID: 38817154, PMCID: PMC11341256, DOI: 10.1002/mrm.30163.Peer-Reviewed Original ResearchStroke volumeHealthy subjectsLong-axis cine imagesTricuspid valve planeTricuspid regurgitation velocityDiastolic function evaluationVentricular stroke volumePhase contrastSystolic excursionRegurgitation velocityTricuspid valveTrace regurgitationDiastolic flowCine imagesTricuspidClinical challengeCardiovascular MRValve planeValvular planeAcquisition planeHigh-velocity jetPatientsTraining deep learning networksPlanimetryDeep learning frameworkAn encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies
Liu Q, Chen Z, Wong H. An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2322376121. PMID: 38809705, PMCID: PMC11161768, DOI: 10.1073/pnas.2322376121.Peer-Reviewed Original ResearchContinuous treatment settingsHigh-dimensional covariate spacesHigh-dimensional covariatesExcess risk boundsLow-dimensional latent spaceEstimate causal effectsDimensional covariatesDeep learning frameworkCovariate spaceRisk boundsGenerative modeling approachNonlinear dimension reductionTreatment settingsBidirectional transformationsLatent spaceLearning frameworkCovariance featuresGenerative modelDimension reductionSuperior performanceCausal effectsLatent variablesCovariatesCovariate adjustmentCausal inferenceA Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks
Batta I, Abrol A, Calhoun V. A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480204.Peer-Reviewed Original ResearchLearning frameworkBrain subsystemsSubspace learning frameworkBrain networksHigh-dimensional neuroimaging dataConvolutional neural networkLow-dimensional subspaceSupervised learning approachDeep learning frameworkStructural brain featuresPredictive performanceUnsupervised approachNeural networkAutomated frameworkDimensional subspaceAlzheimer's diseaseLearning approachBrain changesFeature importanceTraining procedureNeuroimaging dataBrain featuresSalient networkNetworkBrain disorders
2023
Speech Audio Synthesis from Tagged MRI and Non-negative Matrix Factorization via Plastic Transformer
Liu X, Xing F, Stone M, Zhuo J, Fels S, Prince J, El Fakhri G, Woo J. Speech Audio Synthesis from Tagged MRI and Non-negative Matrix Factorization via Plastic Transformer. Lecture Notes In Computer Science 2023, 14226: 435-445. PMID: 38651032, PMCID: PMC11034915, DOI: 10.1007/978-3-031-43990-2_41.Peer-Reviewed Original ResearchWeight mapAudio waveformEnd-to-end deep learning frameworkMatrix factorization-based approachesFactorization-based approachDeep learning frameworkNon-negative matrix factorizationEnd-to-endAdversarial trainingProcess of speech productionTwo-dimensional spectrogramConventional convolutionLearning frameworkMotion featuresTraining samplesAudio synthesisDimension expansionMatrix inputMatrix factorizationTagged MRISpeech productionTransformation modelExperimental resultsSpectrogramPlastic transformationDeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction
Yin Q, Fan R, Cao X, Liu Q, Jiang R, Zeng W. DeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction. Quantitative Biology 2023, 11: 260-274. DOI: 10.15302/j-qb-022-0320.Peer-Reviewed Original ResearchDrug-target interaction predictionDrug-target interactionsConvolutional networkState-of-the-art methodsResidual Graph Convolution NetworkGraph convolutional networkDeep learning frameworkRepresentations of drugsAccurate prediction of drug-drug interactionsDrug repositioningRegression tasksSuperior prediction performanceLearning frameworkComprehensive featuresTop-ranked drugsDiscovery processInteraction predictionDrug discovery processPredictive performanceAccurate predictionNetworkPrediction of drug-drug interactionsDownstream applicationsPrediction of drug interactionsPotential drug candidatesDirect respiratory motion correction of whole-body PET images using a deep learning framework incorporating spatial information
Miao T, Tsai Y, Zhou B, Menard D, Schleyer P, Hong I, Casey M, Liu C. Direct respiratory motion correction of whole-body PET images using a deep learning framework incorporating spatial information. Progress In Biomedical Optics And Imaging 2023, 12463: 124633x-124633x-9. DOI: 10.1117/12.2654472.Peer-Reviewed Original ResearchDeep learning frameworkRespiratory motion correctionMotion-corrected imagesLearning frameworkImage domainSpatial informationData-driven gating methodMotion correctionMotion detection techniqueGround truth imagesU-NetTruth imagesPET imagesData driving methodImage reconstructionWhole-body PET imagesMotion sensorsDetection techniquesExternal motion sensorsCross validationImagesConvenient mannerFrameworkRespiratory motionInformation
2022
Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator
Liu X, Xing F, Prince J, Zhuo J, Stone M, El Fakhri G, Woo J. Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator. Lecture Notes In Computer Science 2022, 13436: 376-386. PMID: 36820764, PMCID: PMC9942274, DOI: 10.1007/978-3-031-16446-0_36.Peer-Reviewed Original ResearchAudio waveformEnd-to-end deep learning frameworkAdversarial training approachDeep learning frameworkEnd-to-endTwo-dimensional spectrogramAdversarial networkIntermediate representationLearning frameworkResidual attentionDisentanglement strategyAudio synthesisDataset sizeImprove realismHeterogeneous representationsHeterogeneous translationAttentional strategiesTraining approachExperimental resultsMuscle deformationIntelligible speechMotor control theoriesTagged-MRIRelated-disordersSpeech acousticsTranslator: A Transfer Learning Approach to Facilitate Single-Cell ATAC-Seq Data Analysis from Reference Dataset
Xu S, Skarica M, Hwang A, Dai Y, Lee C, Girgenti MJ, Zhang J. Translator: A Transfer Learning Approach to Facilitate Single-Cell ATAC-Seq Data Analysis from Reference Dataset. Journal Of Computational Biology 2022, 29: 619-633. PMID: 35584295, PMCID: PMC9464368, DOI: 10.1089/cmb.2021.0596.Peer-Reviewed Original ResearchConceptsGraphic Processing Units ParallelismComplex feature interactionsDeep learning frameworkFree software packageFeature interactionsUltra-high dimensionalityLearning frameworkSoftware packageComputational efficiencySeq data analysisScATAC-seq dataDatasetReference datasetReference dataData analysisDownstream analysisCell representationRepresentationParallelismLow signalTranslatorsClusteringDimensionalityNoise ratioNonlinear relationshipTagged-MRI to audio synthesis with a pairwise heterogeneous deep translator
Liu X, Xing F, Stone M, Prince J, Kim J, Fakhri G, Woo J. Tagged-MRI to audio synthesis with a pairwise heterogeneous deep translator. The Journal Of The Acoustical Society Of America 2022, 151: a133-a133. DOI: 10.1121/10.0010891.Peer-Reviewed Original ResearchLatent space featuresEncoder-decoder structureCNN-based encoderSpace featuresDeep learning frameworkTagged MRI sequencesKullback-Leibler divergenceMel-spectrogramSpeech-related disordersLearning frameworkAudio synthesisAudio waveformSpeech productionKullback-LeiblerHeterogeneous representationsEvaluation strategiesIntelligible speechFrameworkTagged-MRISpeechDecodingAudioVisual movementEncodingUtterances
2021
A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech
Woo J, Xing F, Prince J, Stone M, Gomez A, Reese T, Wedeen V, El Fakhri G. A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech. Medical Image Analysis 2021, 72: 102131. PMID: 34174748, PMCID: PMC8316408, DOI: 10.1016/j.media.2021.102131.Peer-Reviewed Original ResearchConceptsNon-negative matrix factorizationSparse Non-negative Matrix FactorizationIterative shrinkage-thresholding algorithmNon-negative matrix factorization frameworkDeep neural networksMatrix factorization frameworkDeep learning frameworkTongue motionIdentified functional unitsGraph regularizationClustering performanceWeight mapLearning frameworkSpectral clusteringNeural networkMatrix factorizationModular architectureIncreased interpretabilityMotion dataFactorization frameworkConvoluted natureComparison methodTagged magnetic resonance imagingMuscle coordination patternsSpeech
2019
ML-Net: multi-label classification of biomedical texts with deep neural networks
Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. Journal Of The American Medical Informatics Association 2019, 26: 1279-1285. PMID: 31233120, PMCID: PMC7647240, DOI: 10.1093/jamia/ocz085.Peer-Reviewed Original ResearchConceptsMulti-label classificationML-NetBiomedical textEnd deep learning frameworkMulti-label text classificationDeep learning frameworkDeep neural networksTraditional machineDocument contextFeature engineeringText classificationTextual documentsMachine learningNovel endLearning frameworkPrediction networkIndividual classifiersNeural networkHuman effortTarget documentsF-measureArt methodsPrediction mechanismContextual informationLabel countsIntegrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text
Li Z, Yang Z, Shen C, Xu J, Zhang Y, Xu H. Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BMC Medical Informatics And Decision Making 2019, 19: 22. PMID: 30700301, PMCID: PMC6354333, DOI: 10.1186/s12911-019-0736-9.Peer-Reviewed Original ResearchConceptsShortest dependency pathConvolutional neural networkNeural network architectureNatural language processingSentence sequenceRelation extractionClinical relation extractionTarget entityNetwork architectureClinical textNeural networkRepresentation moduleDependency pathsDeep learning-based approachNew neural network architectureBidirectional long short-term memory networkLong short-term memory networkDeep learning frameworkDeep neural networksShort-term memory networkLearning-based approachNovel neural approachRelation extraction datasetBi-LSTM networkSyntactic features
2018
Combining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks
Dvornek NC, Ventola P, Duncan JS. Combining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 725-728. PMID: 30288208, PMCID: PMC6166875, DOI: 10.1109/isbi.2018.8363676.Peer-Reviewed Original ResearchAutism spectrum disorderRecurrent neural networkNeural networkAutism Brain Imaging Data ExchangeSingle deep learning frameworkHeterogeneity of ASDFunctional magnetic resonance imagingDeep learning frameworkResting-state fMRI dataResting-state functional magnetic resonance imagingBetter classification accuracyAutism classificationSpectrum disorderData exchangeLearning frameworkFMRI dataClassification accuracyCross-validation frameworkChallenging taskStraightforward taskPrior workNetworkSuch dataRsfMRITask
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