A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network
Moreover, in case studies of gastric cancer, glioma and breast cancer, there are 19, 17 and 16 out of top 20 candidate lncRNAs inferred by CNMCLDA having been confirmed by recent relevant literatures separately, which demonstrated the outstanding performance of CNMCLDA as well. Hence, it is obvious that CNMCLDA may be an effective tool for prediction of potential lncRNA-disease associations in the future.
Authors
- Sisi Chen (ORCID: https://orcid.org/0000-0003-0761-3291)
- Y. F. Xu (ORCID: https://orcid.org/0000-0002-2394-2522)
- Shunxian Zhou
- Lei Wang (ORCID: https://orcid.org/0000-0002-5065-8447)
- Jenny Le (ORCID: https://orcid.org/0009-0004-9133-6403)
Institutions
- Changsha University (CN)
- First Affiliated Hospital of Hunan University of Traditional Chinese Medicine (CN)
- Shanghai Tunnel Engineering Rail Transit Design & Research Institute (CN)
- Changsha University of Science and Technology (CN)
- Hunan Women'S University (CN)
Publication Details
- Journal
- Frontiers in Genetics
- Published
- 2025-04-09
- DOI
- https://doi.org/10.3389/fgene.2025.1580512
- Citations
- 1
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
- Field-Weighted Citation Impact
- 0.94
Funders
- National Natural Science Foundation of China