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

SIMPLE SUMMARY: Understanding the relationships between genes is crucial to identify the genetic program of organisms. This research could potentially have implications for this understanding. This study aims to build a genetic network for the yeast genome by recognizing interacting genes. This is performed by incorporating different mathematical methodologies and classification techniques to predict relations between two genes. High accuracy is achieved across several tests to identify network interactions. Our findings provide new insights and potential interactions between genes in the yeast genome network. Our results are highly relevant to the field, as they emphasize the power of classification and its role in improving the current understanding of the yeast genome network. 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.