MFPred: prediction of ncRNA families based on multi-feature fusion

Abstract

Abstract Non-coding RNA (ncRNA) plays a critical role in biology. ncRNAs from the same family usually have similar functions, as a result, it is essential to predict ncRNA families before identifying their functions. There are two primary methods for predicting ncRNA families, namely, traditional biological methods and computational methods. In traditional biological methods, a lot of manpower and resources are required to predict ncRNA families. Therefore, this paper proposed a new ncRNA family prediction method called MFPred based on computational methods. MFPred identified ncRNA families by extracting sequence features of ncRNAs, and it possessed three primary modules, including (1) four ncRNA sequences encoding and feature extraction module, which encoded ncRNA sequences and extracted four different features of ncRNA sequences, (2) dynamic Bi_GRU and feature fusion module, which extracted contextual information features of the ncRNA sequence and (3) ResNet_SE module that extracted local information features of the ncRNA sequence. In this study, MFPred was compared with the previously proposed ncRNA family prediction methods using two frequently used public ncRNA datasets, NCY and nRC. The results showed that MFPred outperformed other prediction methods in the two datasets.