Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient.
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.
Department of Software Engineering, College of IT, University of Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Malaysia. Electronic address: faridahh@uniten.edu.my.
Faridah Hani
FH
Mohamed Salleh
Centre of Artificial Intelligence, Faculty of Information Sciences & Technology, Universiti Kebangsaan Malaysia, 43650 UKM Bangi, Malaysia. Electronic address: shereena.arif@ukm.edu.my.
Shereena Mohd
SM
Arif
Centre of Artificial Intelligence, Faculty of Information Sciences & Technology, Universiti Kebangsaan Malaysia, 43650 UKM Bangi, Malaysia. Electronic address: suhaila.zainudin@ukm.edu.my.
Suhaila
S
Zainudin
School of Biosciences & Biotechnology, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia. Electronic address: firdaus@mfrlab.org.
Mohd
M
Firdaus-Raih
D000465:Algorithms; D019295:Computational Biology; D055786:Gene Knockout Techniques; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic
Bioinformatics; DREAM; Gaussian model; Gene regulatory network; Pearson Correlation Coefficient; Probability and statistics