# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 2 Materials and Methods
### Embedding Method  
The miRNA sequence, composed of nucleotide bases A, U, C, and G, plays a crucial role in gene expression regulation. Likewise, gene sequences, which dictate protein synthesis, are formed from the bases A, T, C, and G within DNA. In a deep learning model, the proper representation of miRNAs and genes is crucial as it has a significant impact on the predictive performance of the model. In many previous studies, one-hot encoding was used to represent the sequence data [17]. One-hot encoding is specialized for representing categorical data, in which each category of data is represented by a unique index. However, one-hot encoding does not express the relationships among data and has the disadvantage that the dimension of the vector is equal to the number of data types, which increases the dimensionality. With the advancement in natural language processing, various embedding techniques have been developed. Among them are Word2 Vec [31] and Doc2 Vec [11]. Word2 Vec is a technique recently employed for embedding sequence data encompassing gene sequences. Word2 Vec is an algorithm that generates the embedding of words by using the surrounding words to predict the center word (CBOW) or by using the center word to predict the surrounding words (skip-gram). Word2 Vec has limitations in that it is not sensitive to context and word order; therefore, it was deemed unsuitable for this study, as it predicts associations using miRNA and gene sequence information alone. Doc2 Vec can be seen as an extension of Word2 Vec, designed to embed an entire document within a continuous vector space. It employs two primary architectures: distributed memory (DM) and distributed bag of words (DBOW). DM predicts words in light of the document's sequential order, while DBOW estimates the document as a whole, sidelining individual words and relying on the overall context. Consequently, when it comes to capturing the holistic meaning and structure of a sequence, Doc2 Vec often surpasses Word2 Vec.  
In this study, we utilized a Doc2 Vec-based embedding method, protein2 Vec [32], which more effectively captures the order and flow of sequence data. protein2 Vec is an embedding method for the better prediction of specific properties of proteins and has excellent embedding qualities. We assumed that protein2 Vec, which is based on Doc2 Vec, would be effective for embedding not only protein sequences but also nucleotide sequences; hence, we employed it to embed the data in this study. We embedded 2656 miRNAs and 14,319 genes in 64 dimensions (Figure 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Count of Data Elements** & **Database** \\ \hline (a) Positive Relation & 358,864 & miRTarBase [6] \\ (b) miRNA Sequence & 2656 & miRBase [7] \\ (c) Gene Sequence & 14,319 & biomaRt [8] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data produced by leveraging open datasets.  
Figure 1: The structural representation of the original sequence dataset for miRNA and genes. This figure also explains the embedding method employed in this study, showcasing how the data is transformed into vector values (64 dimensions). Additionally, this figure depicts the number of miRNA and gene sequences utilized in the research.