# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 1 Introduction  
MicroRNAs (miRNAs) are small non-coding RNA molecules comprising approximately 22 nucleotides; they can regulate gene expression and are employed in the development of therapeutics for intractable diseases [1]. Unlike conventional RNAs, miRNAs function to control protein expression by binding to messenger RNAs (mRNAs) and regulating gene expression through RNA interference. The human miRNA sequence consists of the four nucleotide bases: adenine (A), uracil (U), cytosine (C), and guanine (G), which are crucial for the regulation of gene expression. Currently, there are approximately 2000 known species of human miRNAs that play a key role in survival. miRNAs play pivotal roles in diverse biological processes, including development, signal transduction, immunity, nervous system formation, cell proliferation, differentiation, and death. miRNAs have been used to develop innovative therapeutics for various intractable diseases, including cancer. They have been detected in body fluids, such as blood, urine, and saliva, and are used as diagnostic biomarkers for various diseases. Owing to improved comprehension of their role through basic research and also their clinical importance, miRNAs have increasingly been the subject of studies in the past few years [2]. miRNAs have been recognized as playing important roles in the pathogenesis of several diseases. The identification of disease-specific miRNAs, especially for diseases with ineffective therapies, is valuable. Consequently, predicting miRNA-disease associations has gained attention in research. From 2017 to 2023, 29 studies were conducted to predictthe association of miRNAs with different diseases [3]. Acquiring information on molecules related to microRNAs and diseases is essential for miRNA-disease association studies [4]. Over 2000 types of miRNAs have been recognized in humans; however, the features of each one are not completely understood because of complex associations with related molecules and, thus, must be studied further. Previously, determining the association between miRNAs and genes using biological experiments was complicated and challenging in traditional wet labs; these evaluations were time-consuming and expensive. Recently, the prediction of various genomic associations has been undertaken using computational methods that can consider and process a large quantity of biological information [5]. Various embedding methods and learning algorithms applied to open datasets can be utilized to construct mathematical in silico models for predicting the potential relationships between miRNAs and their interacting molecules [4]. Deep learning models are well-suited for handling complex biological information, identifying features, and predicting relationships. Prediction of the association between miRNAs and genes using a deep learning model involves the following steps. First, a dataset is constructed to train the deep learning model. Three datasets are essential to convert biological datasets into input data for deep learning model association prediction: (a) a dataset evidencing a proven association between miRNAs and genes, (b) a dataset possessing biological information on miRNAs, and (c) a dataset possessing biological information on genes. At this stage of the conversion, a researcher should choose the biological information to utilize, such as sequence, structure information, etc. Once these datasets have been constructed, a labeling step for supervised learning is essential. Constructing a training dataset involves both positive and negative labeling. Positive labeling information exists formally; however, negative labeling information does not exist in nature and so must be carefully curated. The next step is data embedding, in which each feature is extracted and vectorized to use the dataset to train the deep learning model. Once embedding is complete according to the labeled data, the data are used for training to create a deep learning model that predicts associations.