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Updated: Aug 23, 2025
In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
Published on: October 21, 2022
XGBCDA: a multiple heterogeneous networks-based method for predicting circRNA-disease associations
Siyuan Shen1, Junyi Liu2, Cheng Zhou2
1School of Software, Xinjiang University, Wulumuqi, 830091, China.
Insights
A new computational model, XGBCDA, efficiently predicts circular RNA (circRNA)-disease associations. This method aids researchers by identifying potential links, reducing experimental costs and time.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Circular RNAs (circRNAs) are crucial in biological processes and diseases.
- Experimental detection of circRNA-disease associations is time-consuming and expensive.
Purpose of the Study:
- To develop an efficient computational model for predicting circRNA-disease associations.
- To reduce the cost and time associated with experimental methods.
Main Methods:
- Proposed a multiple heterogeneous networks-based method (XGBCDA).
- Extracted statistical and graph theory features from integrated circRNA and disease networks.
- Utilized XGBoost classifier to learn latent features and combined them with original features for prediction.
Main Results:
- Achieved an Area Under the ROC Curve of 0.9860 in tenfold cross-validation.
- Demonstrated strong performance in case studies for colorectal, gastric, and cervical cancers.
Conclusions:
- XGBCDA effectively predicts potential circRNA-disease associations.
- The method shows promise in assisting biomedical researchers in this field.
Background:
Biological experiments have demonstrated that circRNA plays an essential role in various biological processes and human diseases. However, it is time-consuming and costly to merely conduct biological experiments to detect the association between circRNA and diseases. Accordingly, developing an efficient computational model to predict circRNA-disease associations is urgent.
Methods:
In this research, we propose a multiple heterogeneous networks-based method, named XGBCDA, to predict circRNA-disease associations. The method first extracts original features, namely statistical features and graph theory features, from integrated circRNA similarity network, disease similarity network and circRNA-disease association network, and then sends these original features to the XGBoost classifier for training latent features. The method utilizes the tree learned by the XGBoost model, the index of leaf that instance finally falls into, and the 1 of K coding to represent the latent features. Finally, the method combines the latent features from the XGBoost with the original features to train the final model for predicting the association between the circRNA and diseases.
Results:
The tenfold cross-validation results of the XGBCDA method illustrate that the area under the ROC curve reaches 0.9860. In addition, the method presents a striking performance in the case studies of colorectal cancer, gastric cancer and cervical cancer.
Conclusion:
With fabulous performance in predicting potential circRNA-disease associations, the XGBCDA method has the promising ability to assist biomedical researchers in terms of circRNA-disease association prediction.
