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• W2951844873 abstract "Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and find improved performance in learning directed networks. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series data set to infer the underlying network structure. Our method is able to improve performance on synthetic data by explicitly modelling the statistical distribution of the data when learning networks from RNA-Seq time series. Applying approximate inference techniques we can learn network structures quickly with only moderate computing resources." @default.
• W2951844873 created "2019-06-27" @default.
• W2951844873 creator A5004571210 @default.
• W2951844873 date "2018-04-11" @default.
• W2951844873 modified "2023-10-06" @default.
• W2951844873 title "Approximate inference of gene regulatory network models from RNA-Seq time series data" @default.
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• W2951844873 doi "https://doi.org/10.1186/s12859-018-2125-2" @default.
• W2951844873 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5896118" @default.
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• W2951844873 hasPublicationYear "2018" @default.
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