Hansheng Xue

TL;DR: A novel semi-supervised autoencoder method to integrate multiple networks and generate a low-dimensional feature representation and a convolutional neural network based on the integrated feature embedding to annotate unlabeled gene functions are designed.

TL;DR: Wang et al. as discussed by the authors proposed a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery.

TL;DR: PhenoSim, a new similarity measure that includes a noise reduction component to model the noisy patient phenotype data, and a path-constrained Information Content-based method for phenotype semantics similarity measurement, could effectively improve the performance of HPO-based phenotype similarity Measurement, thus increasing the accuracy of phenotype-based causative gene prediction and disease prediction.

TL;DR: A novel dynamic heterogeneous network embedding method, termed as DyHATR, which uses hierarchical attention to learn heterogeneous information and incorporates recurrent neural networks with temporal attention to capture evolutionary patterns is proposed.

TL;DR: Compared with five existing state-of-the-art methods, DisPheno shows great performance in HPO-based phenotype semantic similarity measurement and improves the efficiency of disease identification, especially on noisy patients dataset.