# Gene Regulatory Network Inference with Latent Force Models

arXiv: https://arxiv.org/abs/2010.02555 · HTML: https://mlsanity.com/paper/2010.02555/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Approximate Latent Force Model Inference](https://mlsanity.com/paper/2109.11851/) (2021)

## Abstract (arXiv)

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.