Negatif olmayan matris faktorizasyonuna dayalı LncRNA-Hastalık ilişkisi tahmini.

Many biological experiments have proven that lncRNA is related to various complex human diseases. Therefore, knowing the lncRNA-disease relationships not only facilitates the diagnosis, treatment and prognosis of the disease helps to understand the disease mechanism. However, determining the lncRNA-disease relationships through biological experiments is both costly and timeconsuming. For this reason, many researchers have suggested calculational methods to forecast potential relationships between lncRNAs and diseases. In this study, we suggest a computational method named NMF to forecast possible lncRNAs, based on the assumption that functionally similar lncRNAs tend to associate with phenotypically similar diseases. This method integrates the lncRNA expression similarity network, the lncRNA cosine similarity network, the disease semantic similarity network, the disease cosine similarity network, and the known lncRNA-disease relationship network. To demonstrate the prediction accuracy of our method, we applied 5-fold crossvalidation and leave-out cross-validation techniques and obtained ROC plots. AUC of 0.7837 for 5-fold crossvalidation and 0.8551 AUC for leave-out cross-validation were calculated. The results show that the NMF method has reliable prediction performance. [ABSTRACT FROM AUTHOR]