Prediction of miRNA-disease associations based on PCA and cascade forest
Based on PCA and optimized cascade forests, we propose the PCACFMDA model for predicting undiscovered miRNA-disease associations. The experimental results demonstrate superior prediction performance and commendable stability. Consequently, the PCACFMDA is a potent instrument for in-depth exploration of miRNA-disease associations.
Authors
- Chang-Qing Yu (ORCID: https://orcid.org/0009-0007-6655-1744)
- Yubo Li (ORCID: https://orcid.org/0000-0002-9135-8360)
- Chuanlei Zhang (ORCID: https://orcid.org/0000-0002-2053-2755)
- Wei Chen (ORCID: https://orcid.org/0000-0003-3348-4710)
- Yinglun Dong
Institutions
- Tianjin University of Science and Technology (CN)
- China University of Mining and Technology (CN)
- Xijing University (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1186/s12859-024-05999-w
- Citations
- 5
- Primary Topic
- MicroRNA in disease regulation
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- National Natural Science Foundation of China