Jing Zhang, MBA, MPhil, MS
Cards
About
Research
Publications
2024
Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation
Duan Z, Riffle D, Li R, Liu J, Min M, Zhang J. Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Bioinformatics 2024, 40: btae339. PMID: 38806165, DOI: 10.1093/bioinformatics/btae339.Peer-Reviewed Original ResearchExpression measurementsSingle-cell RNA-seq dataCell-to-cell interactionsRNA-seq dataGene expression landscapeGene expression measurementsGene expression changesSpatial transcriptomics technologiesGene imputationState-of-the-art imputation methodsGene expression signaturesExpression landscapeTranscriptomic technologiesExpression changesExpression similaritySpatial transcriptomicsGenesSpatial proximityExpression signaturesExpressionNative microenvironmentTranscriptomeSpeciesSub-cellular resolutionGraph learning methodsSingle-cell genomics and regulatory networks for 388 human brains
Emani P, Liu J, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee C, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken T, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard J, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman G, Huang A, Jiang Y, Jin T, Jorstad N, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran J, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan A, Riesenmy T, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini K, Wamsley B, Wang G, Xia Y, Xiao S, Yang A, Zheng S, Gandal M, Lee D, Lein E, Roussos P, Sestan N, Weng Z, White K, Won H, Girgenti M, Zhang J, Wang D, Geschwind D, Gerstein M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Brennand K, Capauto D, Champagne F, Chatzinakos C, Chen H, Cheng L, Chess A, Chien J, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duong D, Eagles N, Edelstein J, Galani K, Girdhar K, Goes F, Greenleaf W, Guo H, Guo Q, Hadas Y, Hallmayer J, Han X, Haroutunian V, He C, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hyde T, Iatrou A, Inoue F, Jajoo A, Jiang L, Jin P, Jops C, Jourdon A, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Li J, Li M, Lin X, Liu S, Liu C, Loupe J, Lu D, Ma L, Mariani J, Martinowich K, Maynard K, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Mukamel E, Nairn A, Nemeroff C, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Pinto D, Pochareddy S, Pollard K, Pollen A, Przytycki P, Purmann C, Qin Z, Qu P, Raj T, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Voloudakis G, Wang T, Wang S, Wang Y, Wei Y, Weimer A, Weinberger D, Wen C, Whalen S, Willsey A, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang Y, Ziffra R, Zeier Z, Zintel T. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384: eadi5199. PMID: 38781369, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchConceptsSingle-cell genomicsSingle-cell expression quantitative trait locusExpression quantitative trait lociDrug targetsQuantitative trait lociPopulation-level variationSingle-cell expressionCell typesDisease-risk genesTrait lociGene familyRegulatory networksGene expressionCell-typeMultiomics datasetsSingle-nucleiGenomeGenesCellular changesHeterogeneous tissuesExpressionCellsChromatinLociMultiomicsCharacterizing dysregulations via cell-cell communications in Alzheimer’s brains using single-cell transcriptomes
Lee C, Riffle D, Xiong Y, Momtaz N, Lei Y, Pariser J, Sikdar D, Hwang A, Duan Z, Zhang J. Characterizing dysregulations via cell-cell communications in Alzheimer’s brains using single-cell transcriptomes. BMC Neuroscience 2024, 25: 24. PMID: 38741048, PMCID: PMC11089696, DOI: 10.1186/s12868-024-00867-y.Peer-Reviewed Original ResearchConceptsSingle-cell sequencing technologiesSequencing technologiesRisk genesDiversity of gene expression patternsCell typesAD risk genesBrain Initiative Cell Census NetworkCell type-specific mannerCell-to-cell communication networksCanonical cell typesSingle-nucleus RNA sequencingGene expression patternsCell-to-cell communicationCell-cell communicationGenomic resolutionGenes APPSingle-cell transcriptomicsAD brainSignaling networksGene TREM2SnRNA-seqSignaling genesNon-neuronal cellsCellular signalingAlzheimer brainsGene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches
Rahimzadeh N, Srinivasan S, Zhang J, Swarup V. Gene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches. Alzheimer's & Dementia 2024, 20: 3587-3605. PMID: 38534018, PMCID: PMC11095483, DOI: 10.1002/alz.13790.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseGene regulatory networksSingle-cell RNA sequencingAmyloid-beta plaquesMulti-omics approachSingle-cell omicsChromatin stateRegulatory networksGene networksRNA transcriptsSystems biologyBeta plaquesRNA sequencingMulti-omicsField of ADGene responsesSpatial omicsSingle cellsBulk transcriptomesGenetic factorsGenesOmicsAlzheimerChromatinEpigenomescENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding
Duan Z, Xu S, Srinivasan S, Hwang A, Lee C, Yue F, Gerstein M, Luan Y, Girgenti M, Zhang J. scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding. Briefings In Bioinformatics 2024, 25: bbae096. PMID: 38493342, PMCID: PMC10944576, DOI: 10.1093/bib/bbae096.Peer-Reviewed Original ResearchConceptsA/B compartmentsEpigenetic dataChromatin interaction frequenciesCell type-specific mannerChromatin conformational changesGenome binsGenomic regionsChromatin conformationEukaryotic DNAChromatin compartmentsDynamic compartmentalizationRepressed stateGenetic blueprintTranscriptional programsTranscriptional changesChromatinConformational changesComplex tissuesInteraction frequencyCompartmentGenomeChromosomeStructural heterogeneityDNAA/B
2023
ExAD-GNN: Explainable Graph Neural Network for Alzheimer’s Disease State Prediction from Single-cell Data
Duan Z, Lee C, Zhang J. ExAD-GNN: Explainable Graph Neural Network for Alzheimer’s Disease State Prediction from Single-cell Data. APSIPA Transactions On Signal And Information Processing 2023, 12: e201. DOI: 10.1561/116.00000239.Peer-Reviewed Original ResearchScRNA-seq dataDisease state predictionLarge-scale scRNA-seq dataExpression profiling of individual cellsCell type-specific marker genesAD pathologyProfiling of individual cellsAD risk genesSingle-cell sequencing dataAlzheimer's diseaseSequence dataRisk genesMarker genesExpression profilesMolecular insightsGraph neural networksAD diagnosisEarly diagnosis of ADIndividual cellsNeurodegenerative disordersCell typesDisease mechanismsK-Nearest NeighbourCellular levelComplex human brain