2019
Finding Archetypal Spaces Using Neural Networks
van Dijk D, Burkhardt D, Amodio M, Tong A, Wolf G, Krishnaswamy S. Finding Archetypal Spaces Using Neural Networks. 2019, 00: 2634-2643. DOI: 10.1109/bigdata47090.2019.9006484.Peer-Reviewed Original ResearchCompressed Diffusion
Gigante S, Stanley J, Vu N, van Dijk D, Moon K, Wolf G, Krishnaswamy S. Compressed Diffusion. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030994.Peer-Reviewed Original ResearchData regionsModern data analysisDiffusion mapsMost kernel methodsDiffusion geometryHeavy computational loadData pointsRelated embeddingsKernel-based methodsCubic complexityDiffusion map embeddingBig datasetsCorrelation kernelLower dimensionSpectral embeddingComputational loadKernel methodDiffusion relationManifold learningLocal geometryDiffusion processEmbeddingTheoretical connectionsGeometryIntrinsic structureModeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
Gigante S, van Dijk D, Moon K, Strzalkowski A, Wolf G, Krishnaswamy S. Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030978.Peer-Reviewed Original ResearchStochastic dynamic systemsDeep generative neural networksProbability distributionDynamic systemsMarkov modelGlobal dynamicsLocal snapshotGenerative neural networksKalman filterSnapshot dataNeural networkNeural network frameworkRecurrent neural networkSuch systemsNext stateModeling networkNetwork frameworkDynamicsShallow modelsLocal transitionsHypothetical trajectoryModelBiological systemsNatural sciencesLongitudinal measurements
2018
Manifold learning-based methods for analyzing single-cell RNA-sequencing data
Moon K, Stanley J, Burkhardt D, van Dijk D, Wolf G, Krishnaswamy S. Manifold learning-based methods for analyzing single-cell RNA-sequencing data. Current Opinion In Systems Biology 2018, 7: 36-46. DOI: 10.1016/j.coisb.2017.12.008.Peer-Reviewed Original ResearchSingle-cell RNA-sequencing dataSingle-cell RNA sequencing technologyRNA sequencing technologyRNA-sequencing dataThousands of cellsGene regulationCellular statesPhenotypic diversityCellular developmentGene interactionsSequencing technologiesGene expressionSeq dataUnderlying biological signalManifold learning-based methodsSingle experimentBiological signalsRecent advancesDiversityDeeper insightRegulationExpression