Next Article in Journal
Perinatal Lead (Pb) Exposure and Cortical Neuron-Specific DNA Methylation in Male Mice
Previous Article in Journal
Delayed Feeding Alters Transcriptional and Post-Transcriptional Regulation of Hepatic Metabolic Pathways in Peri-Hatch Broiler Chicks
Article Menu

Export Article

Open AccessArticle
Genes 2019, 10(4), 273;

Prediction of Long Non-Coding RNAs Based on Deep Learning

School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
Author to whom correspondence should be addressed.
Received: 12 March 2019 / Revised: 29 March 2019 / Accepted: 29 March 2019 / Published: 3 April 2019
(This article belongs to the Special Issue RNA Target Prediction Methods)
PDF [1393 KB, uploaded 24 April 2019]
  |     |  


With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify long non-coding RNAs (lncRNAs) from transcripts is a challenging task. The identification and inclusion of lncRNAs not only can more clearly help us to understand life activities themselves, but can also help humans further explore and study the disease at the molecular level. At present, the detection of lncRNAs mainly includes two forms of calculation and experiment. Due to the limitations of bio sequencing technology and ineluctable errors in sequencing processes, the detection effect of these methods is not very satisfactory. In this paper, we constructed a deep-learning model to effectively distinguish lncRNAs from mRNAs. We used k-mer embedding vectors obtained through training the GloVe algorithm as input features and set up the deep learning framework to include a bidirectional long short-term memory model (BLSTM) layer and a convolutional neural network (CNN) layer with three additional hidden layers. By testing our model, we have found that it obtained the best values of 97.9%, 96.4% and 99.0% in F1score, accuracy and auROC, respectively, which showed better classification performance than the traditional PLEK, CNCI and CPC methods for identifying lncRNAs. We hope that our model will provide effective help in distinguishing mature mRNAs from lncRNAs, and become a potential tool to help humans understand and detect the diseases associated with lncRNAs. View Full-Text
Keywords: deep learning; long non-coding RNAs; k-mer; BLSTM; CNN; GloVe deep learning; long non-coding RNAs; k-mer; BLSTM; CNN; GloVe

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Liu, X.-Q.; Li, B.-X.; Zeng, G.-R.; Liu, Q.-Y.; Ai, D.-M. Prediction of Long Non-Coding RNAs Based on Deep Learning. Genes 2019, 10, 273.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Genes EISSN 2073-4425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top