Detecting lncRNA-Cancer Associations by Combining miRNAs, Genes, and Prognosis With Matrix Factorization.

ABSTRACT: Motivation: Long non-coding RNAs (lncRNAs) play important roles in cancer development. Prediction of lncRNA-cancer association is necessary for efficiently discovering biomarkers and designing treatment for cancers. Currently, several methods have been developed to predict lncRNA-cancer associations. However, most of them do not consider the relationships between lncRNA with other molecules and with cancer prognosis, which has limited the accuracy of the prediction. Method: Here, we constructed relationship matrices between 1,679 lncRNAs, 2,759 miRNAs, and 16,410 genes and cancer prognosis on three types of cancers (breast, lung, and colorectal cancers) to predict lncRNA-cancer associations. The matrices were iteratively reconstructed by matrix factorization to optimize low-r

SUBMITTER: Yan H 

PROVIDER: S-EPMC8273282 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature