Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations

Abstract

More and more evidences have showed that the unnatural expression of long non-coding RNA (lncRNA) is relevant to varieties of human diseases. Therefore, accurate identification of disease-related lncRNAs can help to understand lncRNA expression at the molecular level and to explore more effective treatments for diseases. Plenty of lncRNA-disease association prediction models have been raised but it is still a challenge to recognize unknown lncRNA-disease associations. In this work, we have proposed a computational model for predicting lncRNA-disease associations based on geometric complement heterogeneous information and random forest. Firstly, geometric complement heterogeneous information was used to integrate lncRNA-miRNA interactions and miRNA-disease associations verified by experiments. Secondly, lncRNA and disease features consisted of their respective similarity coefficients were fused into input feature space. Thirdly, an autoencoder was adopted to project raw high-dimensional features into low-dimension space to learn representation for lncRNAs and diseases. Finally, the low-dimensional lncRNA and disease features were fused into input feature space to train a random forest classifier for lncRNA-disease association prediction. Under five-fold cross-validation, the AUC (area under the receiver operating characteristic curve) is 0.9897 and the AUPR (area under the precision-recall curve) is 0.7040, indicating that the performance of our model is better than several state-of-the-art lncRNA-disease association prediction models. In addition, case studies on colon and stomach cancer indicate that our model has a good ability to predict disease-related lncRNAs.

Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases

“…Random Forest (RF) and Gradient Boosted Trees (GBT) are applied as ML supervised algorithms. The choice of these algorithms is due to their ability to efficiently process heterogeneous attributes [19][20][21][22], including numerical and categorical ones. Specifically, RF is an ensemble ML algorithm consisting of the use of a chosen number of decision trees by combining the outputs of the decision trees into a single result.…”

AMPGLDA: Predicting LncRNA-Disease Associations Based on Adaptive Meta-Path Generation and Multi-Layer Perceptron

“…Drawing upon incremental principal component analysis (PCA) and RF algorithm, Zhu et al [15] proposed a novel integrated machine learning-based approach (IPCARF). Other methods include predicting lncRNA-disease associations through support vector machines (SVMs) [16], random forests (RF) [17][18][19], matrix factorization [20,21], random walks [22][23][24], and naive Bayesian classifiers [25].…”

Predicting lncRNA–Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration

“…Regarding IPCARF, association prediction was performed with the Random Forest technique after feature extraction via PCA. GCHIRFLDA is a method for extracting potential features using an autoencoder and combining it with a Random Forest classifier for prediction. SCCPMD first performed similarity enhancement using logistic functions and then performs prediction of potential association pairs using the probabilistic matrix decomposition method with corrected similarity constraints.…”