Ontological function annotation of lncRNAs through hierarchical multi-label classification

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.

Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here researchers at Central South University 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, the reseachers use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. They 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.

Flowchart of NeuraNetL2GO

It includes four steps: A) Construct the lncRNA similarity network; B) Extract topological features in the network with the DCA approach; C) Build the training dataset by employing the Neighbor Counting method; D)Training the multi-layer neural networks

Availability: The source code and data are available at http://denglab.org/NeuraNetL2GO/.