Jingpu Zhang1,2, Zuping Zhang1, Zixiang Wang3, Yuting Liu3, Lei Deng3,4. 1. School of Information Science and Engineering, Central South University, Changsha 410083, China. 2. School of Computer (Software), Ping Ding Shan University, Pingdingshan 467000, China. 3. School of Software, Central South University, Changsha 410075, China. 4. Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China.
Abstract
Motivation: Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs. Results: Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here we describe NeuraNetL2GO, a computational ontological function prediction approach for lncRNAs using hierarchical multi-label classification strategy based on multiple neural networks. The neural networks are incrementally trained level by level, each performing the prediction of gene ontology (GO) terms belonging to a given level. In NeuraNetL2GO, we use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. We show that NeuraNetL2GO achieves the best performance and the overall advantage in maximum F-measure and coverage on the manually annotated lncRNA2GO-55 dataset compared to other state-of-the-art methods. Availability and implementation: The source code and data are available at http://denglab.org/NeuraNetL2GO/. Contact: leideng@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs. Results: Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here we describe NeuraNetL2GO, a computational ontological function prediction approach for lncRNAs using hierarchical multi-label classification strategy based on multiple neural networks. The neural networks are incrementally trained level by level, each performing the prediction of gene ontology (GO) terms belonging to a given level. In NeuraNetL2GO, we use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. We show that NeuraNetL2GO achieves the best performance and the overall advantage in maximum F-measure and coverage on the manually annotated lncRNA2GO-55 dataset compared to other state-of-the-art methods. Availability and implementation: The source code and data are available at http://denglab.org/NeuraNetL2GO/. Contact: leideng@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.