Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

Abstract

Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.

Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

“…E. coli K-12 MG1655, one of the most well-known E. coli strains, has about 4.6 Mb and contains approximately 4400 genes, highly interacting with each other and shaping various biological networks. Gene regulatory network (GRN) and transcriptional regulatory network (TRN) were constructed to know the regulatory relationship of genes and often reconstructed to improve the prediction of gene expression [ 8 , 9 , 10 , 11 ]. Connecting GRN to the metabolic network, which is consisted of metabolites, was implemented to understand the cell system [ 12 ].…”