  Systems biology is an emerging interdisciplinary area of research that
focuses on study of complex interactions in a biological system, such as gene
regulatory networks. The discovery of gene regulatory networks leads to a wide
range of applications, such as pathways related to a disease that can unveil in
what way the disease acts and provide novel tentative drug targets. In
addition, the development of biological models from discovered networks or
pathways can help to predict the responses to disease and can be much useful
for the novel drug development and treatments. The inference of regulatory
networks from biological data is still in its infancy stage. This paper
proposes a recurrent neural network (RNN) based gene regulatory network (GRN)
model hybridized with generalized extended Kalman filter for weight update in
backpropagation through time training algorithm. The RNN is a complex neural
network that gives a better settlement between the biological closeness and
mathematical flexibility to model GRN. The RNN is able to capture complex,
non-linear and dynamic relationship among variables. Gene expression data are
inherently noisy and Kalman filter performs well for estimation even in noisy
data. Hence, non-linear version of Kalman filter, i.e., generalized extended
Kalman filter has been applied for weight update during network training. The
developed model has been applied on DNA SOS repair network, IRMA network, and
two synthetic networks from DREAM Challenge. We compared our results with other
state-of-the-art techniques that show superiority of our model. Further, 5%
Gaussian noise has been added in the dataset and result of the proposed model
shows negligible effect of noise on the results.
