In this study, we propose a new predictor named iPiDA-LTR (see Fig.1) to identify piRNA-disease associations, which has the following advantages:
(i) iPiDA-LTR predictor combines component methods and learning to rank,
which cannot only identify missing associations between known piRNAs and diseases,
but also can identify diseases associated with newly detected piRNAs;
(ii) The task of identifying piRNA-disease associations is a positive unlabelled learning problem.
The iPiDA-LTR predictor incorporates learning to rank only considering the top ranked positive samples;
(iii) A free web server of iPiDA-LTR has been established at http://bliulab.net/iPiDA-LTR.