Feature-ranking methods for RNA sequencing data

Ribonucleic acid sequencing (RNA-Seq) is a technique that is used a lot to study and evaluate gene expression patterns and find genes that are expressed differently in different biological situations. Numerous computational algorithms for analysing RNA-seq data have been developed that categorise them per features in many pre-defined classifications. Feature-ranking techniques have emerged as a powerful tool for analysing RNA sequencing data, enabling the identification of the most relevant genes that are associated with specific phenotypes or biological processes. In this chapter, we give an overview of different ways to rank features and how they can be used to analyse data from RNA sequencing. We also compare how well different methods work using benchmark datasets and talk about the difficulties of combining multiple data sources and figuring out what the results mean. Last, we talk about possible future directions for the development and use of feature-ranking techniques. These include the use of deep learning techniques, the use of single-cell sequencing data, and the development of methods for figuring out how genes interact with each other. We evaluate selected features by optimising parameters and identifying a higher-performing classifier. The accuracy, recall, false-positive rate (FPR), and precision are used to analyse the comparison. The chapter aims to provide a comprehensive guide for researchers who want to use feature-ranking techniques to analyse RNA sequencing data and gain insights into the underlying biology.