# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 1 Introduction
Recently, various deep learning studies have been conducted using this approach. First, there are several studies dedicated to predicting the association between miRNAs and genes. The SG-LSTM-FRAME model [4] was developed to predict the association between miRNAs and genes; it utilizes both sequential and mechanistic features of miRNAs and genes to predict their relationship. miRNA-gene relationship information was obtained from miRarBase [6], and sequence information was obtained from miRBase [7] and biomaRt [8]. Negative data were generated from positive data by setting a threshold using Euclidean distance [9] and cosine similarity [10] to create a negative sample. Each piece of sequence information was embedded using Doc2 Vec [11], and the miRNA-gene relationship information was embedded using Role2 Vec [12]. The embedded information was used to learn and predict the association between miRNAs and genes using long short-term memory (LSTM) [13]. miTAR [14] uses both spatial and sequential features between miRNAs and genes through a CNN [15] and a bidirectional RNN (Bi-RNN) [16]. DeepMirTar [17] and miRAW [18] were used for miRNA-gene relationship information and miRBase was used for miRNA sequence information. The DeepMirTar dataset was used for negative data. The sequence information was converted into a five-dimensional vector of {A, U, G, C, N} characters in the embedding layer. A CNN was used to learn the spatial features of the miRNA:Target, and a Bi-RNN was used to learn the sequential features of the miRNA:Target. SRG-Vote [19] was implemented using the existing SG-LSTM-WHOLE [4] dataset. After embedding the sequence information using Doc2 Vec, the miRNA-gene relationship information was embedded using various methods, such as Role2 Vec, graph convolutional network (GCN) [20], and node2 vec [21]. Accordingly, the highest area under the receiver operating characteristic curve (AUC) was achieved using a GCN, and GCN embedding was applied using the embedding generated by Role2 Vec as a feature of the node. After that, the experiment was conducted by comparing Bi-LSTM [22] and LSTM. Second, there are studies aimed at predicting the association between miRNAs and target mRNAs. Inferring miRNA targets based on restricted Boltzmann machines (IMRBM) [23], using machine learning, employed RBM to predict miRNA targets. They obtained the miRNA-gene relationship data from miRNABase and BioGRID [24] and the corresponding sequence information from miRBase and NCBI [25]. A weighted miRNA-mRNA interaction network was manually constructed to utilize the prediction results of multiple methods, weighting miRNA-mRNA pairs according to their frequency. A weighted network was used to train the model and make predictions using restricted Boltzmann machines. DeepTarget [26] is a recurrent neural network (RNN) [27] framework for miRNA-mRNA relationship prediction. It was constructed using a miRNA target-related dataset from miRRecords [28] and miRNA sequence information from miRBase. Negative data for the experiment were randomly generated using the miRRandom algorithm after random sequence generation using the Fisher-Yates shuffle algorithm. Sequence information was encoded as a four-dimensional dense vector that was randomly initialized and trained using the gradient descent method, and the encoded information was trained using an RNN-based autoencoder to predict the interaction between miRNA and mRNA. DeepMirTra predicts miRNA-mRNA relationships. Information on the miRNA-mRNA relationship was obtained from mirMark and CLASH data, and sequence information was obtained from miRBase and the UCSC Genome Browser. The negative data were randomly sampled using the miRRandom algorithm. Each piece of sequence information was embedded using one-hot encoding, and an autoencoder was used for prediction. Lastly, there is a study predicting the association between miRNAs and long non-coding RNA (lncRNA).