A Weighted Bagging LightGBM Model for Potential lncRNA-Disease Association Identification

Abstract

There is increasing evidence that long non-coding RNAs (lncRNAs) are closely related to many human diseases. Developing powerful computational models for potential lncRNA-disease association identification would facilitate biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention. Now there exist a number of methods specially for this problem based on inductive matrix completion, random walk or classification. In terms of this issue, classification has just come to the fore. Extracting important features from disease network and RNA network, namely network embedding, is the top priority. Moreover, taking the complexity into consideration, genetic algorithm is adopted to tune the hyper-parameters of our network embedding model. Due to a lack of negative samples, we also exploit Positive-Unlabeled (PU) learning to help out. In brief, we propose a weighted bagging lightGBM model for lncRNA-disease association prediction based on network embedding and PU learning.