Long noncoding RNAs (lncRNAs) are emerging as a novel class of noncoding RNAs 
and potent gene regulators, which play an important and varied role in cellular 
functions. lncRNAs are closely related with the occurrence and development of 
some diseases. High-throughput RNA-sequencing techniques combined with de novo 
assembly have identified a large number of novel transcripts. The discovery of 
large and 'hidden' transcriptomes urgently requires the development of effective 
computational methods that can rapidly distinguish between coding and long 
noncoding RNAs. In this study, we developed a powerful predictor (named as 
lncRNA-MFDL) to identify lncRNAs by fusing multiple features of the open reading 
frame, k-mer, the secondary structure and the most-like coding domain sequence 
and using deep learning classification algorithms. Using the same human training 
dataset and a 10-fold cross validation test, lncRNA-MFDL can achieve 97.1% 
prediction accuracy which is 5.7, 3.7, and 3.4% higher than that of CPC, CNCI 
and lncRNA-FMFSVM predictors, respectively. Compared with CPC and CNCI 
predictors in other species (e.g., anole lizard, zebrafish, chicken, gorilla, 
macaque, mouse, lamprey, orangutan, xenopus and C. elegans) testing datasets, 
the new lncRNA-MFDL predictor is also much more effective and robust. These 
results show that lncRNA-MFDL is a powerful tool for identifying lncRNAs. The 
lncRNA-MFDL software package is freely available at for academic users.