Jun Yin

TL;DR: A computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNAs association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and mi RNAs into a heterogeneous network is developed.

TL;DR: A novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA) is proposed, which innovatively built a computational framework integrating ensemble learning and dimensionality reduction.

TL;DR: Adaptive Boosting for MiRNA-Disease Association prediction (ABMDA) is developed to predict potential associations between diseases and miRNAs and was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights.

TL;DR: A computational model of Random Forest for miRNA-disease association (RFMDA) prediction based on machine learning is developed and the results of cross-validation and case studies indicated that RFMDA is a reliable model for predicting mi RNA- disease associations.

TL;DR: An unsupervised deep learning model of the variational autoencoder for MiRNA–disease association prediction (VAEMDA) that can avoid noise introduced by the random selection of negative samples and reveal associations between miRNAs and diseases from the perspective of data distribution is presented.