Abstract
Accumulating researches have found that lncRNAs play a key role in many important biological processes, such as chromatin modification, transcription, and post-transcription regulation. Because lncRNAs play an important role in the life process, many important complex diseases have been linked to the variation and dysfunction of lncRNAs. In current prediction researches on lncRNA-disease association, clinical prognosis information of the disease (such as pathological stage, clinical stage and so on) is rarely mentioned. In this manuscript, we apply the pathological stage data into the lncRNA-disease association prediction. Firstly, coordinates reverse rotation in circular (CRRC) is proposed. 6 clusters are calculated by the proposed cluster generating algorithm (ClGeA) based on CRRC. Secondly, harmonic importance ranking (HIR) is put forward. 28 core variables are obtained by the proposed selection algorithm of core variables for cancer pathological stage (SA-CV-CPS) based on HIR and cluster. Finally, on the basis of the above 28 core variables, pathological stage prediction algorithm for lncRNA-disease association based on principal component regression analysis (PSPA-LA-PCRA) is developed. Through PSPA-LA-PCRA, principal component set (including 20 PCs) and prediction model are gained. The proposed prediction model is based on unknown human lncRNA-disease association combining with the pathological stage data. Experimental results show that better results for AUC, precision rate, recall rate and F1-score of the prediction model are achieved by PSPA-LA-PCRA, which provides a favorable research premise for subsequent prediction studies of lncRNA-disease associations.
Highlights
Long non-coding RNA is a class of RNA molecules that are longer than 200nt and non-coding for proteins [1]–[3]
A pathological stage prediction model for Long non-coding RNA (lncRNA)-disease association based on principal component regression analysis was constructed for the lncRNA-disease association prediction
Pathological stage prediction algorithm for lncRNA-disease association based on principal component regression analysis was named PSPA-LA-PCRA in short
Summary
Long non-coding RNA (lncRNA) is a class of RNA molecules that are longer than 200nt and non-coding for proteins [1]–[3]. A new method was proposed to predict potential lncRNA-disease associations. Lan et al [23] proposed a new method of identifying lncRNA-disease associations by collaborative deep learning, which is called LDICDL. Chen et al [24] built a novel prediction method for lncRNA-disease association based on the lncRNA similarities, disease similarities and the support vector machine (ILDMSF in short). Li et al [27] built a new heterogeneous network to predict the potential lncRNA-disease associations and proposed a new model called LRWHLDA. LRWHLDA used an improved local random walk to achieve high prediction precision It was suitable for lacking known lncRNA-disease associations. Our method correlated the pathological stage data that was treated as a decision attribute, a pathological stage prediction algorithm for lncRNA-disease association was built.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
