Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient. (December 2015)

• Record Type: Journal Article
• Authors: Mohamed Salleh, Faridah Hani; Arif, Shereena Mohd; Zainudin, Suhaila; Firdaus-Raih, Mohd
• Is Part Of: Computational biology and chemistry. Volume 59:Part B(2015)
• Page Start: 3
• Page End: 14
• Publication Date: 2015-12
• Subjects: Bioinformatics -- Probability and statistics -- DREAM -- Gene regulatory network -- Gaussian model -- Pearson Correlation Coefficient; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.04.012
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A method to reconstruct gene regulatory networks from knock-out data is proposed. The proposed method apply Normal Distributions and Pearson Correlation Coefficient. Indirect regulations generate many false positives. Most of the false negatives are mostly due to multiple genes input. Abstract: A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.