Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

ABSTRACT: RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological class

SUBMITTER: Johnson NT 

PROVIDER: S-EPMC6097660 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature