2020
Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood
Li S, Crawford FW, Gerstein MB. Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood. Nature Communications 2020, 11: 3575. PMID: 32681003, PMCID: PMC7368050, DOI: 10.1038/s41467-020-17388-x.Peer-Reviewed Original Research
2017
Birth/birth-death processes and their computable transition probabilities with biological applications
Ho LST, Xu J, Crawford FW, Minin VN, Suchard MA. Birth/birth-death processes and their computable transition probabilities with biological applications. Journal Of Mathematical Biology 2017, 76: 911-944. PMID: 28741177, PMCID: PMC5783825, DOI: 10.1007/s00285-017-1160-3.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsBayes TheoremCommunicable DiseasesComputational BiologyComputer SimulationEnglandEpidemicsHistory, 17th CenturyHost-Parasite InteractionsHumansLikelihood FunctionsMarkov ChainsMathematical ConceptsModels, BiologicalMonte Carlo MethodPlagueProbabilityStochastic ProcessesConceptsBirth-death processTransition probabilitiesFinite-time transition probabilitiesSIR modelMonte Carlo approximationJoint posterior distributionLikelihood-based inferenceApproximate Bayesian computationStatistical inferenceMatrix exponentiationPosterior distributionProcess approximationBivariate extensionBayesian computationFraction representationLaplace transformCorrelation structureUnivariate populationsRemoved (SIR) modelSmall systemsBivariate processEfficient algorithmApproximationDirect inferenceFast method