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Benchmarking of computational methods for predicting circRNA-disease associations
Wei Lan1, Yi Dong1, Hongyu Zhang1
1School of Computer, Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi 530004, China.
Briefings in Bioinformatics
|January 7, 2023
Summary
Computational methods for identifying circular RNA (circRNA)-disease associations offer faster alternatives to experiments. This study categorizes, compares, and evaluates these methods, providing insights into their performance and future directions for circRNA research.
Area of Science:
- Biochemistry
- Bioinformatics
- Genomics
Background:
- Circular RNAs (circRNAs) are increasingly recognized for their roles in human diseases.
- Experimental identification of circRNA-disease associations is time-consuming.
- Computational methods are emerging as efficient alternatives.
Purpose of the Study:
- To systematically categorize existing computational methods for circRNA-disease association prediction.
- To compare the performance of different categories of methods (information propagation, traditional machine learning, deep learning).
- To evaluate method effectiveness using multiple datasets and cross-validation strategies.
Main Methods:
- Categorization of computational methods into information propagation, traditional machine learning, and deep learning.
- Selection and detailed introduction of baseline methods within each category.
- Comparative analysis of 14 representative methods across 5 datasets using 5-fold, 10-fold cross-validation, and de novo experiments.
Main Results:
- Performance evaluation of selected methods across different datasets and validation strategies.
- Comparison of correctly identified circRNA-disease associations for six common cancers.
- Observations on the robustness and characteristics of various computational approaches.
Conclusions:
- Summary of the strengths and weaknesses of different computational methods for circRNA-disease association prediction.
- Identification of key challenges and future research directions in the field.
- Highlighting the potential of computational approaches for advancing circRNA-disease research.
