# Bayesian variable selection in linear dynamical systems

@article{Aalto2018BayesianVS, title={Bayesian variable selection in linear dynamical systems}, author={Atte Aalto and Jorge M. Gonçalves}, journal={arXiv: Methodology}, year={2018} }

We develop a method for reconstructing regulatory interconnection networks between variables evolving according to a linear dynamical system. The work is motivated by the problem of gene regulatory network inference, that is, finding causal effects between genes from gene expression time series data. In biological applications, the typical problem is that the sampling frequency is low, and consequentially the system identification problem is ill-posed. The low sampling frequency also makes it…

## 4 Citations

### Continuous time Gaussian process dynamical models in gene regulatory network inference

- Computer Science
- 2018

A GRN inference method called BINGO is developed, based on MCMC sampling of trajectories of the GPDM and estimating the hyperparameters of the covariance function of the Gaussian process, which is superior in dealing with poor time resolution and computationally feasible.

### Linear system identification from ensemble snapshot observations

- Computer Science2019 IEEE 58th Conference on Decision and Control (CDC)
- 2019

Two different paradigms are studied for linear system identification, based on tracking the evolution of the distribution of cells over time and the so-called pseudotime concept, identifying a common trajectory through the state space, along which cells propagate with different rates.

### Network Stability, Realisation and Random Model Generation

- Computer Science2019 IEEE 58th Conference on Decision and Control (CDC)
- 2019

This work provides procedures to obtain "stable" DSF models or require the presence of feedback structures while keeping topology and dynamics random up to these constraints and suggests model generation algorithms, whose implementations are now publicly available.

### Gene regulatory network inference from sparsely sampled noisy data

- Computer ScienceNature Communications
- 2020

BINGO is presented, a powerful method for network inference from time series data that clearly and consistently outperforms state-of-the-art methods and is available to any researcher, helping to decipher the complex mechanisms of life.

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