LncMirNet: Predicting LncRNA-miRNA Interaction Based on Deep Learning of Ribonucleic Acid Sequences.

Sen Yang,Dan Shao,Kai He,Yu Lin,Lan Huang,Yan Wang

doi:10.3390/molecules25194372

Abstract

Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing that the interaction patterns between lncRNAs and miRNAs are highly related to cancer development, gene regulation, cellular metabolic process, etc. Contemporaneously, with the rapid development of RNA sequence technology, numerous novel lncRNAs and miRNAs have been found, which might help to explore novel regulated patterns. However, the increasing unknown interactions between lncRNAs and miRNAs may hinder finding the novel regulated pattern, and wet experiments to identify the potential interaction are costly and time-consuming. Furthermore, few computational tools are available for predicting lncRNA–miRNA interaction based on a sequential level. In this paper, we propose a hybrid sequence feature-based model, LncMirNet (lncRNA–miRNA interactions network), to predict lncRNA–miRNA interactions via deep convolutional neural networks (CNN). First, four categories of sequence-based features are introduced to encode lncRNA/miRNA sequences including k-mer (k = 1, 2, 3, 4), composition transition distribution (CTD), doc2vec, and graph embedding features. Then, to fit the CNN learning pattern, a histogram-dd method is incorporated to fuse multiple types of features into a matrix. Finally, LncMirNet attained excellent performance in comparison with six other state-of-the-art methods on a real dataset collected from lncRNASNP2 via five-fold cross validation. LncMirNet increased accuracy and area under curve (AUC) by more than 3%, respectively, over that of the other tools, and improved the Matthews correlation coefficient (MCC) by more than 6%. These results show that LncMirNet can obtain high confidence in predicting potential interactions between lncRNAs and miRNAs.

Highlights

Noncoding RNAs [1] cannot encode proteins, they play indispensable roles in numerous life processes [2,3,4,5,6,7]
Long non-coding RNA (LncRNA) and miRNA, as two typical ncRNAs, are proof related to cancer development, gene regulation, cellular metabolic process, etc. miRNA is a small ncRNA with 20–25 nt adhering to lncRNA to indirectly regulate gene expression [5], adjust lncRNA
= 256) with forwarding step one were used, so altogether, 340 features were generated to represent a lncRNA sequence, while only 1-mer, 2-mer, and 3-mer were used for a miRNA sequence due to the short length of miRNA

Summary

Introduction

Noncoding RNAs (ncRNAs) [1] cannot encode proteins, they play indispensable roles in numerous life processes [2,3,4,5,6,7]. Accumulated studies show that many ncRNAs are involved in various life regulation processes [8,9]. LncRNA and miRNA, as two typical ncRNAs, are proof related to cancer development, gene regulation, cellular metabolic process, etc. MiRNA is a small ncRNA with 20–25 nt adhering to lncRNA (more than 200 nt) to indirectly regulate gene expression [5], adjust lncRNA function, and cooperate with lncRNA to finish regulation processes. The increasing evidence shows that the interaction between lncRNA and miRNA contributes to finding some potential regulation. Exploring lncRNA–miRNA interactions can support the understanding of some of the complicated functions between lncRNAs and miRNAs. In earlier studies, researchers mainly explored unknown lncRNA–miRNA interactions through laboratory experiments. Finding potential interaction between lncRNAs and miRNAs by a biological laboratory is labor-intensive, time-consuming, and costly. In 2018, Huang et al introduced a group preference Bayesian collaborative filtering model (GBCF) for picking up a top-k probability ranking list for an individual miRNA or lncRNA based on the known miRNA–lncRNA interaction network [10]

Methods

Results

Discussion

Conclusion