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.
Highlights
In the human genome, more than 98% of the genes are noncoding protein sequences
Based on the assumption that there is a potential association between an long noncoding RNAs (ncRNAs) (lncRNA) and a disease, if they are associated with the same set of miRNAs, similar diseases tend to be closely related to IneRNAs with similar functions; the method LDLMD is proposed by (Wang et al, 2019)
Number of associations 276 319 621 these problems, 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
Summary
More than 98% of the genes are noncoding protein sequences. The remaining 2% can only be transcribed into noncoding RNAs (ncRNAs). Based on the combination of incremental principal component analysis (IPCA) and random forest (RF), a lncRNA–disease association prediction method IPCARF is proposed by Zhu et al (2021). The model GAERF is proposed by Wu et al (2021); GAREF uses graph autocoding (GAE) and RF to identify disease-related lncRNAs. A random walk-based multi-similarity fusion and bidirectional label propagation method RWSF-BLP is proposed by Xie et al (2021). Based on the assumption that there is a potential association between an lncRNA and a disease, if they are associated with the same set of miRNAs, similar diseases tend to be closely related to IneRNAs with similar functions; the method LDLMD is proposed by (Wang et al, 2019). Number of associations 276 319 621 these problems, a method based on the Gradient Boosting Decision Tree (GBDT) and LR to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper.
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