English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In recent years, it has been increasingly clear that long non-coding RNAs (lncRNAs) are able to regulate their target genes at multi-levels, including transcriptional level, translational level, etc and play key regulatory roles in many important biological processes, such as cell differentiation, chromatin remodeling and more. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method by alternating least squares based on matrix factorization to predict lncRNA-disease associations, referred to as ALSBMF. ALSBMF first decomposes the known lncRNA-disease correlation matrix into two characteristic matrices, then defines the optimization function using disease semantic similarity, lncRNA functional similarity and known lncRNA-disease associations and solves two optimal feature matrices by least squares method. The two optimal feature matrices are finally multiplied to reconstruct the scoring matrix, filling the missing values of the original matrix to predict lncRNA-disease associations. Compared to existing methods, ALSBMF has the same advantages as BPLLDA. It does not require negative samples and can predict associations related to novel lncRNAs or novel diseases. In addition, this study performs leave-one-out cross-validation (LOOCV) and five-fold cross-validation to evaluate the prediction performance of ALSBMF. The AUCs are 0.9501 and 0.9215, respectively, which are better than the existing methods. Furthermore colon cancer, kidney cancer, and liver cancer are selected as case studies. The predicted top three colon cancer, kidney cancer, and liver cancer-related lncRNAs were validated in the latest LncRNADisease database and related literature. In order to test the ability of ALSBMF to predict novel disease-associated lncRNAs and new lncRNA-associated diseases, all known associations of diseases and lncRNAs were eliminated, the predicted top five breast cancer, nasopharyngeal carcinoma cancer-related lncRNAs and top five H19, MALAT1 lncRNA-related cancers were validated in PubMed and dbSNP.

ALSBMF: Predicting lncRNA-Disease Associations by Alternating Least Squares Based on Matrix Factorization