Gene Regulatory Network Inference with Latent Force Models

• URL: http://arxiv.org/abs/2010.02555v1
• Date: Tue, 6 Oct 2020 09:03:34 GMT
• Title: Gene Regulatory Network Inference with Latent Force Models
• Authors: Jacob Moss, Pietro Li\'o
• Abstract summary: Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data. We present a model which incorporates translation delays by combining mechanistic equations and Bayesian approaches to fit to experimental data.
• Score: 1.2691047660244335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data. Accurate GRNs can be very insightful when modelling development, disease pathways, and drug side-effects. We present a model which incorporates translation delays by combining mechanistic equations and Bayesian approaches to fit to experimental data. This enables greater biological interpretability, and the use of Gaussian processes enables non-linear expressivity through kernels as well as naturally accounting for biological variation.

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