identifying piwi-interacting RNA-disease associations based on learning to rank

| Home | Server | Dataset | Tutorial | Citation |


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.

iPiDA-LTR web server
Fig.1. The workflow of iPiDA-LTR predictor. (a) Association feature extraction: piRNA sequences and disease ontology are used to calculate piRNA sequence similarities and disease semantic similarities by combining them and piRNA-disease associations to construct association features and labels; (b) Component methods: four methods are used to train models with benchmark dataset, and then trained models are utilized to calculate association scores of query piRNAs; (c) Predicting candidate diseases associated with query piRNAs: association scores of samples in the benchmark dataset are used to train LambdaMART model, and then trained LambdaMART model are employed to rank candidate diseases associated with query piRNAs.