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

Abstract

In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease 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 lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample 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 networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis

“…The continuous decline of the loss function can improve the performance of the model, and it is advisable to decrease the loss function along the gradient direction. Gradient boosting is a framework that can fit a number of different algorithms into it and the GBDT algorithm has the following advantages [17]: a high prediction accuracy; suitable for low dimensional data; able to deal with nonlinear data; a flexible processing of diverse data types such as discrete and continuous data; a great accuracy for a low decision time; the use of certain robust loss functions; and robustness to outliers [17]. Here, we processed a total of 48 key DEGs with preferable diagnostic value, compared seven machine learning algorithms, and eventually applied the GBDT machine learning algorithm to build a diagnostic model with three genes, PBRM1, CA1 and TXLNG, that had a significant differential expression between PAH and control samples and were finally regarded as molecular biomarkers of Group I PAH.…”

SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network

“…The internal inclined random walk with restart (IIRWR) is used by Wang et al ( 16 ) to infer potential lncRNA-disease associations. A lncRNAs-disease association prediction method GBDTLRL2D based on Gradient Boosting Decision Tree and Logistic Regression is proposed by Duan et al ( 17 ). The GCRFLDA, a prediction method based on graph convolution matrix completion, is proposed by Fan et al ( 18 ).…”