GENE DELETION DATA BASED GENOMIC REGULATORY NETWORK INFERENCE

The gene deletion data is a type of gene expression data, which is obtained by deleting each gene consecutively from the network and measuring the fitness of the remaining network under various environmental conditions. Compared to the microarray data, the deletion data is much easier and economical to obtain. The gene tag technology has enabled the deletion data to be largely available for various regulatory networks. However, very few inference algorithms are proposed for the deletion data in spite of its advantages. In this paper, we propose an inference algorithm based on gene deletion data. The proposed inference algorithm capture the dynamical and non-linear natures of the regulatory networks. We conduct experiment on the GAL network to demonstrate the performance of the proposed algorithm. The proposed algorithm has been shown to serve as a good alternatives for exploring various regulatory networks other than using microarray data.