Gene Regulatory Network Inference with Latent Force Models
This work addresses a specific bottleneck in bioinformatics for researchers modeling development, disease pathways, and drug side-effects, but it appears incremental as it builds on existing methods by incorporating delays.
The paper tackled the problem of inferring Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data by addressing delays in protein synthesis, resulting in a model that improves biological interpretability and accounts for biological variation.
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.