Inference and analysis of gene regulatory networks using data mining techniques

Abstract

Causality draws the relationship between the cause and the effect among a pair of items. Causality inference in Gene Regulatory Networks (GRNs) is a challenging task due to the limits of appropriate data and large dataset size of real networks. Such an inference is very important because it might give the blueprint of how a complex organism operates which might further reveal the probable cause of some of the terminal diseases. A number of techniques have been proposed and applied to infer causal relationships in various domains. However, they are not specific to regulatory network inference. In our work, we made an empirical study on a few selective methods in inferring causal GRNs. To assess the candidate methods in terms of their quality of inference we used time-series expression data from the DREAM challenges. Our findings revealed that among the selected causality finding techniques, Causation Entropy gives the best result, however, it is computationally expensive. We used the second-best approach, Transfer Entropy, to infer casual GRN of breast cancer datasets. The topological analysis of the network revealed that top out-degree genes such as SLC39A5 which are considered central genes, plays an important role in cancer progression. The main intention behind this research work was to develop an effective inference method for causal Gene Regulatory Network which is computationally less expensive and also considers the effects of time-delay during regulation of one gene by the other. We proposed a novel time delayed inferring method for GRNs using association rule mining. We tried to infer the regulatory sign of the edges i.e. whether influencing positively or negatively. Comparative results highlight that our proposed technique produced better inference results in comparison to several of the existing techniques. We applied the proposed method to perform post inference analysis on malignant cancer network and benign network. Various networks parameters like the degree distribution, average path length, clustering coefficient, network re-wiring structure analysis, network topological variation analysis, and connectivity variation analysis are performed to identify the differentially expressed genes between the two networks. To avoid inference biasness and to obtain a consensus based conclusion we made a comparative analysis between our proposed method and transfer entropy based method and also computed the average score. Such an analysis of the network has revealed four genes, namely, STON 2 , UMODL 1 , STK 11 , and COBL which are already under investigation for its role in various types of cancer as seen from various researches.