Multilayer Perceptron based Model of Large-Scale Gene Regulatory Network

Abstract

Background: The computational reconstruction of Gene Regulatory Networks (GRNs) using different techniques have encountered the challenge of constructing large network because of many parameters to be fitted and the nature of the input data. In fact, contemporary works on GRN inference that involve the use of hybridized techniques especially Artificial Neural Network (ANN) with meta-heuristic optimization techniques have to trade off computational cost for accuracy in reconstructing large-scale GRN. This work designed an efficient feature selection algorithm with GRN model to overcome the dimension problem of input data using biological prior knowledge of co-expression and network sparseness, so as to capture and represent the actual interrelationship among genes. Methodology: The GRN model is an ensemble Multi-Layer Perceptron (MLP) network incorporating a novel feature selection algorithm termed Fuzzified Adjusted Rand Index (FARI). FARI is developed to investigate and establish the expression trends of genes in an expression profile data. A rank matrix of all genes produced by FARI shows their co-expression