The Implementation of Deep Learning Algorithm with Gaussian Blur Data Preprocessing in Circular RNA Classification and Detection

Abstract

Introduction: Circular RNAs (circRNAs) are increasingly recognized as key regulators of gene expression due to their unique closed-loop structure and involvement in various cellular processes. This study investigates the utilization of machine learning algorithms in predicting circRNA-disease associations.

Methods: This study proposes a novel deep learning approach leveraging artificial neural networks (ANN) for circRNA classification. The methodology involves data collection from circRNA databases, k-mers counting for feature extraction, Gaussian blur implementation for data smoothing, and ANN-based model training.

Results: Evaluation of the trained models based on precision, recall, and f1-score metrics shows an overall accuracy of 0.7511, with an average precision score of 0.7982, recall of 0.7511, and f1-score of 0.7637.

Discussion: The results indicate that our ANN-based algorithm effectively detects and classifies circRNA datasets with considerable accuracy. Compared to the algorithm from past research, our algorithm is also shown to have less computational power.

Conclusion: Comparative analysis demonstrates improved performance compared to previous algorithms, suggesting its potential for widespread implementation due to reduced computational requirements and simpler implementation.