Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling
Lee H, Emani P, Gerstein M. Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling. Journal Of Chemical Information And Modeling 2024 PMID: 39576762, DOI: 10.1021/acs.jcim.4c01116.Peer-Reviewed Original ResearchBinding affinity predictionAffinity predictionMeta-modelMeta-modeling approachLigand-protein binding affinityState-of-the-art deep learning toolsState-of-the-artBinding affinityDeep learning modelsDeep learning toolsMolecular descriptorsInclusion of featuresVirtual screeningBase modelDatabase scalabilityGeneralization capabilityDiverse modeling approachesTraining databaseApplication benchmarksDrug ligandsLearning modelsLigandPhysicochemical propertiesLearning toolsDevelopment effortsGENCODE 2025: reference gene annotation for human and mouse
Mudge J, Carbonell-Sala S, Diekhans M, Martinez J, Hunt T, Jungreis I, Loveland J, Arnan C, Barnes I, Bennett R, Berry A, Bignell A, Cerdán-Vélez D, Cochran K, Cortés L, Davidson C, Donaldson S, Dursun C, Fatima R, Hardy M, Hebbar P, Hollis Z, James B, Jiang Y, Johnson R, Kaur G, Kay M, Mangan R, Maquedano M, Gómez L, Mathlouthi N, Merritt R, Ni P, Palumbo E, Perteghella T, Pozo F, Raj S, Sisu C, Steed E, Sumathipala D, Suner M, Uszczynska-Ratajczak B, Wass E, Yang Y, Zhang D, Finn R, Gerstein M, Guigó R, Hubbard T, Kellis M, Kundaje A, Paten B, Tress M, Birney E, Martin F, Frankish A. GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Research 2024, gkae1078. PMID: 39565199, DOI: 10.1093/nar/gkae1078.Peer-Reviewed Original ResearchGene annotationLong-read transcriptome sequencingMulti-genome alignmentsRibo-Seq experimentsUCSC Genome BrowserState-of-the-art proteomicsGenome browserRibo-seqSpecies genomesMouse genomeTranscriptome sequencingGENCODEGenomeAnnotation workflowAnnotationSequencePangenomeMiceGenesetsState-of-the-artUCSCProteomicsTranscriptionGenesSpeciesA variational graph-partitioning approach to modeling protein liquid-liquid phase separation
Wang G, Warrell J, Zheng S, Gerstein M. A variational graph-partitioning approach to modeling protein liquid-liquid phase separation. Cell Reports Physical Science 2024, 5: 102292. DOI: 10.1016/j.xcrp.2024.102292.Peer-Reviewed Original ResearchPredicting 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 network