Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-Coding RNA Identification.

ABSTRACT: Long non-coding RNAs (lncRNAs) play crucial roles in many biological processes and are implicated in several diseases. With the next-generation sequencing technologies, substantial unannotated transcripts have been discovered. Classifying unannotated transcripts using biological experiments are more time-consuming and expensive than computational approaches. Several tools are available for identifying long non-coding RNAs. These tools, however, did not explain the features in their tools that contributed to the prediction results. Here, we present Xlnc1DCNN, a tool for distinguishing long non-coding RNAs (lncRNAs) from protein-coding transcripts (PCTs) using a one-dimensional convolutional neural network with prediction explanations. The evaluation results of the human test set showed that

SUBMITTER: Lin R 

PROVIDER: S-EPMC9173695 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature