Faculty, Staff and Student Publications
Language
English
Publication Date
3-29-2023
Journal
Biology
DOI
10.3390/biology12040518
PMID
37106719
PMCID
PMC10135911
PubMedCentral® Posted Date
3-29-2023
PubMedCentral® Full Text Version
Post-print
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.
Keywords
bioinformatics, RNA-seq, microarray, regulatory networks, kernel classification, gene expression profiling
Published Open-Access
yes
Recommended Citation
Al-Aamri, Amira; Kudlicki, Andrzej S; Maalouf, Maher; et al., "Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification" (2023). Faculty, Staff and Student Publications. 749.
https://digitalcommons.library.tmc.edu/uthshis_docs/749