DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization.

ABSTRACT:

Background

In 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).

Results

In 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_2,1-norm i

SUBMITTER: Liu JX 

PROVIDER: S-EPMC8114493 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature