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 L2,1-norm is added in our method. At the same time, Gaussian interaction profile kernel is added to our method, which increase the network similarity between lncRNA and disease. Finally, the AUC value obtained by the experiment is used to evaluate the quality of our method, and the AUC value is obtained by the ten-fold cross-validation method.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.
Highlights
In the development of science and technology, there are increasing evidences that there are some associations between long non-coding RNAs (lncRNAs) and human diseases
In the second part of this paper, we show the experimental results of the dual sparse collaborative matrix factorization (DSCMF) method
Human LncRNA‐disease associations The LncRNADisease database is a common database for studying lncRNA-disease associations [29]
Summary
In the development of science and technology, there are increasing evidences that there are some associations between lncRNAs and human diseases. Finding these associations between them will have a huge impact on our treatment and prevention of some diseases. It is important to find some good methods for predicting lncRNA-disease associations (LDAs). Science and technology have developed rapidly, and many experts and scholars are paying more and more attention to long non-coding RNAs (lncRNAs). There are increasing evidences that lncRNAs are closely linked to many human diseases, such as common cardiovascular diseases [5, 6], diabetes [7], Alzheimer’s [8] and some cancers. It is very necessary to find a method for efficient and accurate LDAs prediction
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