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Bottom-Up Ggm Algorithm For Constructing Multilayered Hierarchical Gene Regulatory Networks That Govern Biological Pathways Or Processes, Sapna Kupari, Wenping Deng, Chathura J. Gunasekara, Vincent Chiang, Huann-Sheng Chen, Hairong Wei, Et. Al.

Michigan Tech Publications

Background: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.

Results: A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct an ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory …