DeepmiRNATar: A deep learning-based model for miRNA targets prediction

  • Huimin Peng School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China
  • Chenyu Li School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China
  • Ying Lu School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China
  • Dazhou Li School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China
Keywords: MiRNA-mRNAs prediction; deep learning; TextCNN; BiLSTM; SpatialConv attention
Article ID: 253

Abstract

MicroRNAs (miRNAs) play a crucial role in regulating fundamental biological processes such as the cell cycle, differentiation, and apoptosis by directly interacting with multiple genes (mRNAs). This regulatory mechanism has a profound impact on cellular function and the overall physiological condition of an organism. However, the prediction of miRNA-mRNA interactions encounters computational challenges in the field of biology due to the diverse sequences and complex data patterns. To overcome these obstacles, this research effort introduced DeepmiRNATar, a tool designed to precisely pinpoint miRNA targets, offering essential assistance in the realm of disease management. DeepmiRNATar leverages the Word2vec-based DeepLncLoc approach for encoding miRNA sequence characteristics and utilizes the DNABERT pre-trained model for in-depth semantic comprehension of target sequences. Through the integration of TextCNN, BiLSTM, and SpatialConv Attention mechanisms, the model scrutinizes structural features, temporal relationships, and overall interactions within the sequences. Following a series of experimental assessments, DeepmiRNATar attained an impressive AUC of 99.15% on the evaluation dataset, on par with the current leading prediction methodologies. Notably, the precision-recall curve, sensitivity, and F-measure values reached 99.18%, 97.43%, and 95.47%, respectively. Compared to existing miRNA target prediction models, DeepmiRNATar demonstrates a notable enhancement in overall predictive accuracy. The successful creation and experimental validation of the DeepmiRNATar model signify a significant advancement in miRNA target identification technology.

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Published
2024-11-14
How to Cite
Peng, H., Li, C., Lu, Y., & Li, D. (2024). DeepmiRNATar: A deep learning-based model for miRNA targets prediction. Molecular & Cellular Biomechanics, 21(3), 253. https://doi.org/10.62617/mcb253
Section
Article