  Motivation: Since long non-coding RNAs (lncRNAs) have involved in a wide
range of functions in cellular and developmental processes, an increasing
number of methods have been proposed for distinguishing lncRNAs from coding
RNAs. However, most of the existing methods are designed for lncRNAs in animal
systems, and only a few methods focus on the plant lncRNA identification.
Different from lncRNAs in animal systems, plant lncRNAs have distinct
characteristics. It is desirable to develop a computational method for accurate
and robust identification of plant lncRNAs. Results: Herein, we present a plant
lncRNA identification method ItLnc-BXE, which utilizes multiple features and
the ensemble learning strategy. First, a diversity of lncRNA features is
collected and filtered by feature selection to represent RNA transcripts. Then,
several base learners are trained and further combined into a single
meta-learner by ensemble learning, and thus an ItLnc-BXE model is constructed.
ItLnc-BXE models are evaluated on datasets of six plant species, the results
show that ItLnc-BXE outperforms other state-of-the-art plant lncRNA
identification methods, achieving better and robust performances (AUC>95.91%).
We also perform some experiments about cross-species lncRNA identification, and
the results indicate that dicots-based and monocots-based models can be used to
accurately identify lncRNAs in lower plant species, such as mosses and algae.
Availability: source codes are available at
https://github.com/BioMedicalBigDataMiningLab/ItLnc-BXE. Contact:
<EMAIL_ADDRESS>(or<EMAIL_ADDRESS>Supplementary information:
Supplementary data are available at Bioinformatics online.
