ArticleBMC bioinformatics2021
DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- CDELDA: A Content-Based Dual-Encoder for Cold-Start lncRNA-Disease Association Prediction.Biomedicines · 2026Article
- LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.BMC bioinformatics · 2025Article
- GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.BMC bioinformatics · 2024Article
- Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM.Frontiers in genetics · 2023Article
- iLncDA-RSN: identification of lncRNA-disease associations based on reliable similarity networks.Frontiers in genetics · 2023Article
- Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks.BMC bioinformatics · 2022Article
- SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations.Frontiers in microbiology · 2022Article
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5 authors.
Funding
Abstract
backgroundIn the development of science and technology, there are increasing evidences that there are some associations between lncRNAs and human diseases. Therefore, finding these associations between them will have a huge impact on our treatment and prevention of some diseases. However, the process of finding the associations between them is very difficult and requires a lot of time and effort. Therefore, it is particularly important to find some good methods for predicting lncRNA-disease associations (LDAs).
resultsIn this paper, we propose a method based on dual sparse collaborative matrix factorization (DSCMF) to predict LDAs. The DSCMF method is improved on the traditional collaborative matrix factorization method. To increase the sparsity, the L
conclusionsThe AUC value obtained by the DSCMF method is 0.8523. At the end of the paper, simulation experiment is carried out, and the experimental results of prostate cancer, breast cancer, ovarian cancer and colorectal cancer are analyzed in detail. The DSCMF method is expected to bring some help to lncRNA-disease associations research. The code can access the https://github.com/Ming-0113/DSCMF website.
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