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IMPMD: An Integrated Method for Predicting Potential Associations Between miRNAs and Diseases
Meiqi Wu1, Yingxi Yang1, Hui Wang1
11Department of Information and Computer Science, University of Science and Technology Beijing, Beijing100083, China; 2Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China; 3Institute of Computing Technology, Chinese Academy of Sciences, Beijing100080, China.
Background:
With the rapid development of biological research, microRNAs (miRNAs) have increasingly attracted worldwide attention. The increasing biological studies and scientific experiments have proven that miRNAs are related to the occurrence and development of a large number of key biological processes which cause complex human diseases. Thus, identifying the association between miRNAs and disease is helpful to diagnose the diseases. Although some studies have found considerable associations between miRNAs and diseases, there are still a lot of associations that need to be identified. Experimental methods to uncover miRNA-disease associations are time-consuming and expensive. Therefore, effective computational methods are urgently needed to predict new associations.
Methodology:
In this work, we propose an integrated method for predicting potential associations between miRNAs and diseases (IMPMD). The enhanced similarity for miRNAs is obtained by combination of functional similarity, gaussian similarity and Jaccard similarity. To diseases, it is obtained by combination of semantic similarity, gaussian similarity and Jaccard similarity. Then, we use these two enhanced similarities to construct the features and calculate cumulative score to choose robust features. Finally, the general linear regression is applied to assign weights for Support Vector Machine, K-Nearest Neighbor and Logistic Regression algorithms.
Results:
IMPMD obtains AUC of 0.9386 in 10-fold cross-validation, which is better than most of the previous models. To further evaluate our model, we implement IMPMD on two types of case studies for lung cancer and breast cancer. 49 (Lung Cancer) and 50 (Breast Cancer) out of the top 50 related miRNAs are validated by experimental discoveries.
Conclusion:
We built a software named IMPMD which can be freely downloaded from https://github.com/Sunmile/IMPMD.
Insights
This study introduces IMPMD, an integrated computational method to predict microRNA-disease associations. IMPMD achieves high accuracy, aiding in disease diagnosis and identifying novel associations more efficiently than experimental methods.
Area of Science:
- Genomics and Bioinformatics
- Molecular Biology
- Computational Biology
Background:
- MicroRNAs (miRNAs) play crucial roles in biological processes and human diseases.
- Identifying miRNA-disease associations is vital for disease diagnosis.
- Existing experimental methods are costly and time-consuming, necessitating computational approaches.
Purpose of the Study:
- To develop an effective computational method for predicting potential miRNA-disease associations.
- To improve the efficiency and accuracy of identifying novel miRNA-disease links.
Main Methods:
- An integrated method (IMPMD) was developed, combining functional, Gaussian, and Jaccard similarities for miRNAs.
- Disease similarities were computed using semantic, Gaussian, and Jaccard measures.
- Feature construction and robust feature selection were performed, followed by weighted machine learning algorithms (SVM, KNN, Logistic Regression).
Main Results:
- IMPMD achieved an Area Under the Curve (AUC) of 0.9386 in 10-fold cross-validation, outperforming existing models.
- Case studies on lung and breast cancer demonstrated high predictive power, with 49/50 and 50/50 top-ranked miRNAs validated experimentally.
Conclusions:
- The developed IMPMD software provides an efficient and accurate tool for predicting miRNA-disease associations.
- IMPMD can accelerate the discovery of novel miRNA-disease relationships, aiding in diagnostics and research.
- The IMPMD software is available for free download.
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