Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm

TL;DR

This paper presents a novel approach combining Recurrent Neural Networks with Bat Algorithm optimization to infer gene regulatory networks from time series data, demonstrating effectiveness on artificial and real datasets despite noise.

Contribution

It introduces a Bat Algorithm optimized RNN model for gene regulatory network inference, improving accuracy and robustness over traditional methods.

Findings

Successfully inferred gene regulations in artificial networks with noise

Identified maximum true positives in E. coli microarray data

Demonstrated robustness of the method in noisy conditions

Abstract

Correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers. Several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. In this paper, Bat Algorithm (BA) is applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN). Initially the proposed method is tested against small artificial network without any noise and the efficiency is observed in term of number of iteration, number of population and BA optimization parameters. The model is also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset. In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. coli. The results prove that it can able to identify the maximum number of true positive regulation but also include some false positive regulations. Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model.