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

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.

Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction

“…Deep-learning-based methods, also known as deep neural networks, are the extension of artificial neural networks. Data-driven deep learning models have been widely applied in analyzing the RNA sequences, including convolutional neural network (CNN), recurrent neural network (RNN), auto-encoder (AE), and graph convolution network (GCN) [32,33,[51][52][53][54][55][56][57]59,60]. The basic idea can be divided into two steps: training a neural network and making prediction using the trained neural network.…”

“…Conservation-based methods Detecting complementary regions miRU [26], miRNAassist [27] Thermodynamic-based methods Calculating the minimum free energy structure RNAcofold [28], RNAhybird [29] Shallow-machine-learning-based methods Data-driven and feature extraction TargetMiner [30], miTarget [31] Deep-learning-based methods Data-driven and learning the high-level discriminative features MiRTDL [32], LncMirNet [33] Graph-based methods Network inference LCBNI [34], EPLMI [35] For traditional, conservation-based methods, the initial and direct hypothesis about RRI prediction is to use sequence alignment tools such as BLAST [36]. If there is a complementary region in the two RNA sequences, then the two RNAs would be predicted potentially interacting with a high probability.…”

“…In lncRNA-miRNA interaction prediction, some methods [33,54,60] use traditional prediction tools to predict the possibility of a random lncRNA and a random miRNA in the positive dataset and treat lncRNA-miRNA pairs with a low interaction possibility as negative samples. Other methods [55,56] divide lncRNAs into two groups according to existing experimental observations as to whether these lncRNAs interact with miRNAs.…”

Long non-coding RNAs and microRNAs as crucial regulators in cardio-oncology

“…Additional studies are needed to delve deeper into this interplay as downstream targets of sponged miRNAs can also serve as therapeutic targets. As it is beyond the scope of this current review to provide a list of predicted miRNAs that can be sponged, investigators can take advantage of available databases such as LncCeRBase [ 342 ], lncRNASNP2 [ 343 ], DIANA-LncBase v3 [ 344 ], LncMirNet [ 345 ], lnCeDB [ 346 ], SomamiR 2.0, [ 347 ], miRSponge [ 348 ], and starBase V2.0 [ 349 ].…”

WAFNRLTG: A Novel Model for Predicting LncRNA Target Genes Based on Weighted Average Fusion Network Representation Learning Method

“…Based on the embedding results, GEEL used individual graph embedding method-based model as basic predictors and build an ensemble model to predict the potential interactions between lncRNAs and miRNAs. Yang et al (2020) proposed the lncMirNet model which predicted lncRNA-miRNA interactions based on hybrid sequence features. Based on these, Zhao et al (2020) developed a method, named DeepLGP, for prioritizing lncRNA target genes via encoding gene and lncRNA features.…”