Prediction of miRNAs and diseases association based on sparse autoencoder and MLP
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Published on: December 15, 2023
Si-Lin Sun1, Bing-Wei Zhou1, Sheng-Zheng Liu1
1Department of Information Science Technology, Hainan Normal University, Haikou, Hainan, China.
Abstract:
Introduction: MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods. Methods: Therefore, we propose a new sparse autoencoder and MLP method (SPALP) to predict the association between miRNAs and diseases. In this study, we adopt advanced deep learning technologies, including sparse autoencoder and multi-layer perceptron (MLP), to improve the accuracy of predicting miRNA-disease associations. Firstly, the SPALP model uses a sparse autoencoder to perform feature learning and extract the initial features of miRNAs and diseases separately, obtaining the latent features of miRNAs and diseases. Then, the latent features combine miRNAs functional similarity data with diseases semantic similarity data to construct comprehensive miRNAs-diseases datasets. Subsequently, the MLP model can predict the unknown association among miRNAs and diseases. Result: To verify the performance of our model, we set up several comparative experiments. The experimental results show that, compared with traditional methods and other deep learning prediction methods, our method has significantly improved the accuracy of predicting miRNAs-disease associations, with 94.61% accuracy and 0.9859 AUC value. Finally, we conducted case study of SPALP model. We predicted the top 30 miRNAs that might be related to Lupus Erythematosus, Ecute Myeloid Leukemia, Cardiovascular, Stroke, Diabetes Mellitus five elderly diseases and validated that 27, 29, 29, 30, and 30 of the top 30 are indeed associated. Discussion: The SPALP approach introduced in this study is adept at forecasting the links between miRNAs and diseases, addressing the complexities of analyzing extensive bioinformatics datasets and enriching the comprehension contribution to disease progression of miRNAs.
Insights
We developed a novel deep learning method (SPALP) to predict associations between microRNAs (miRNAs) and diseases. This approach significantly improves prediction accuracy, aiding in understanding disease mechanisms and developing new treatments.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are small, non-coding RNA molecules with critical regulatory functions.
- Aberrant miRNA expression is increasingly linked to various human diseases.
- Understanding miRNA-disease associations is vital for elucidating disease pathogenesis and identifying therapeutic targets.
Purpose of the Study:
- To propose and validate a novel computational method for predicting miRNA-disease associations.
- To leverage deep learning techniques for enhanced accuracy in identifying these relationships.
- To provide a tool for discovering potential biomarkers and therapeutic strategies related to miRNAs and diseases.
Main Methods:
- A sparse autoencoder and multi-layer perceptron (MLP) method (SPALP) was developed.
- Sparse autoencoder was used for feature learning and extracting latent features of miRNAs and diseases.
- Latent features were integrated with miRNA functional similarity and disease semantic similarity data to build comprehensive datasets for MLP-based prediction.
Main Results:
- The SPALP method achieved a high prediction accuracy of 94.61% and an AUC value of 0.9859.
- Comparative experiments demonstrated superior performance over traditional and existing deep learning methods.
- Case studies showed successful prediction of top miRNAs associated with five common elderly diseases, with high validation rates.
Conclusions:
- The SPALP approach effectively predicts miRNA-disease associations, offering a robust tool for bioinformatics research.
- This method addresses the challenges of analyzing large-scale omics data.
- SPALP contributes to a deeper understanding of miRNA roles in disease progression and potential clinical applications.
