Semi-Bayesian Inference of Time Series Chain Graphical Models in Biological Networks

Farnoudkia, Hajar
Purutçuoğlu Gazi, Vilda
The construction of biological networks via time-course datasets can be performed both deterministic models such as ordinary differential equations and stochastic models such as diffusion approximation. Between these two branches, the former has wider application since more data can be available. In this study, we particularly deal with the probabilistic approaches for the steady-state or deterministic description of the biological systems when the systems are observed though time. Hence, we consider time series chain graphical model which enables to bind the activation of a system under different time points via two covariance matrices under multivariate normal distributions of states. In inference of this complex model, we propose two scenarios based on two stages. In the first plan, the time courses are supposed as sample. The covariance matrix Γ which connects the system in distinct time points via the vector of autoregressive models with different lags is estimated by the correlation of nodes in consecutive times. Then, the covariance matrix Σ which presents the activation in a single time point is inferred via Bayesian algorithms. In the second plan, Γ is estimated similar to the first plan. But, at the second stage, for each time point, Σ is estimated separately via Bayesian methods, and their union is the final estimation of the system. We perform these strategies under different dimensional and different number of observed time points’ data. The results indicate that while the dimensions of systems increase, the accuracies of estimated systems improve too irrelevant from the number of points and observations.
Citation Formats
H. Farnoudkia and V. Purutçuoğlu Gazi, “Semi-Bayesian Inference of Time Series Chain Graphical Models in Biological Networks,” presented at the International Conference on Innovative Engineering Applications (CIEA’2018) (20 - 22 Eylül 2018), Sivas, Türkiye, 2018, Accessed: 00, 2021. [Online]. Available: