Zheng-Wei Li

TL;DR: The reliable performance of Path-Based MiRNA-Disease Association is demonstrated, which demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

TL;DR: The experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates.

TL;DR: DRMDA is a promising prediction method which could identify potential and novel miRNA–disease associations and is compared with five previous classical prediction models in global leave‐ one‐one‐out cross‐validation (LOOCV), local LOOCV and fivefold cross-validation, respectively.

TL;DR: A novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and the newly developed discrim inative vector machine (DVM) classifier is reported.

TL;DR: A novel graph auto-encoder model, named GAEMDA, is proposed, which applies a graph neural networks-based encoder, which contains aggregator function and multi-layer perceptron for aggregating nodes' neighborhood information, to generate the low-dimensional embeddings of miRNA and disease nodes and realize the effective fusion of heterogeneous information.