AUTHOR=Duan Tao , Kuang Zhufang , Wang Jiaqi , Ma Zhihao 

TITLE=GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.753027

DOI=10.3389/fcell.2021.753027

ISSN=2296-634X

ABSTRACT=In recent years, the long non-coding RNA (lncRNA) have been shown to be involved in many processes of disease. The prediction of the lncRNAs-diseases association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNAs-diseases association seldomly consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve these problem, a method based on the Gradient Boosting Decision Tree (GBDT) and Logistic Regression (LR) to predict the  lncRNAs-diseases association (GBDTLRL2D) is proposed in this paper. Metagraph2Vec is used  for feature learning, and negative samples sets are selected by using K-means clustering. The  innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative  negative sample set, and the use of Metagraph2Vec can better retain the semantic and structural  features in heterogeneous network. The average AUCs of GBDTLRL2D obtained on the three  datasets are 0.98, 0.98, 0.96 in 10-fold cross-validation respectively.