Prediction of Bacterial sRNAs Using Sequence-Derived Features and Machine Learning.

ABSTRACT: Small ribonucleic acid (sRNA) sequences are 50-500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism's genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs pre

SUBMITTER: Jha T 

PROVIDER: S-EPMC9397377 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature