Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Steach H, Viswanath S, He Y, Zhang X, Ivanova N, Hirn M, Perlmutter M, Krishnaswamy S. Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. Lecture Notes In Computer Science 2024, 14758: 235-252. DOI: 10.1007/978-1-0716-3989-4_15.Peer-Reviewed Original ResearchSingle-cell resolutionMetabolic networksStructure of metabolic networksBiological processesGlobal metabolic networkMetabolic stateMeasure gene expressionGenomic informationTranscriptomic dataTranscriptome dataPost-translationallyEpigenetic modificationsMultimodal regulationGene expressionSingle-cellTissue scaleBiological featuresCellsTranscriptomeMetabolomicsTranscriptionFlux ratesMultiomicsScRNAseqBiologySupervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Hodgson L, Li Y, Iturria-Medina Y, Stratton J, Wolf G, Krishnaswamy S, Bennett D, Bzdok D. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression. Communications Biology 2024, 7: 591. PMID: 38760483, PMCID: PMC11101463, DOI: 10.1038/s42003-024-06273-8.Peer-Reviewed Original ResearchConceptsGene programAlzheimer's diseaseLate-onset Alzheimer's diseaseAD risk lociCell type-specificSingle-nucleus RNA sequencingRisk lociAD brainAlzheimer's disease progressionSnRNA-seqRNA sequencingAD pathophysiologySignaling cascadesTranscriptome modulationProgressive neurodegenerative diseaseCell-typeGWASNeurodegenerative diseasesNeuronal lossGlial cellsTranscriptomeLociGenesPseudo-trajectoriesDisease progression