2024
Predicting 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 ResearchConceptsGene expressionSpatial gene expressionSpatial transcriptomics technologiesTissue histology imagesExpressed genesGene activationTranscriptomic technologiesMolecular underpinningsGraph neural networksState-of-the-artSpatial expressionGenesTissue architectureExpressionHistological imagesNeural networkLeveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach
Frank M, Ni P, Jensen M, Gerstein M. Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2320510121. PMID: 39110734, PMCID: PMC11331094, DOI: 10.1073/pnas.2320510121.Peer-Reviewed Original ResearchConceptsProtein phase transitionsAssociated with reduced gene expressionProtein structure predictionAlzheimer's disease-related proteinsDisease-related proteinsAlzheimer's diseaseProtein sequencesSequence variantsStructure predictionAmyloid aggregatesProtein designGene expressionAge-related diseasesNatural defense mechanismsSoluble stateProteinDefense mechanismsBiophysical featuresAlzheimerSequenceAmyloidVariantsExpressionLanguage modelComputational frameworkRepresenting core gene expression activity relationships using the latent structure implicit in Bayesian networks
Gao J, Gerstein M. Representing core gene expression activity relationships using the latent structure implicit in Bayesian networks. Bioinformatics 2024, 40: btae463. PMID: 39051682, PMCID: PMC11316617, DOI: 10.1093/bioinformatics/btae463.Peer-Reviewed Original ResearchTranscriptional regulatory networksGene regulatory networksCo-expression networkGene expression activityChIP-seqGene conservationCluster genesSupplementary dataRegulatory networksBiological networksClearer clusteringCo-expressionExpression activityBioinformaticsGenesBiomedical studiesConservationExpressionClustersSingle-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, PMCID: PMC11365579, 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 tissuesExpressionCellsChromatinLociMultiomics
2006
Genomic analysis of the hierarchical structure of regulatory networks
Yu H, Gerstein M. Genomic analysis of the hierarchical structure of regulatory networks. Proceedings Of The National Academy Of Sciences Of The United States Of America 2006, 103: 14724-14731. PMID: 17003135, PMCID: PMC1595419, DOI: 10.1073/pnas.0508637103.Peer-Reviewed Original ResearchConceptsTranscription factorsMaster transcription factorRegulatory networksRegulatory hierarchyProtein-protein interaction networkMost transcription factorsExpression of thousandsExpression level changesGenomic analysisProtein interactionsInteraction networksTarget genesDirect targetGenesEukaryotesProkaryotesCellsFundamental questionsBiologyTargetExpression