H
Hansheng Xue
Researcher at Northwestern Polytechnical University
Publications - 17
Citations - 263
Hansheng Xue is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Autoencoder & Node (networking). The author has an hindex of 5, co-authored 17 publications receiving 145 citations. Previous affiliations of Hansheng Xue include Australian National University & Harbin Institute of Technology.
Papers
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Journal ArticleDOI
Integrating multi-network topology for gene function prediction using deep neural networks
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.
Journal ArticleDOI
Predicting disease-related genes using integrated biomedical networks
Jiajie Peng,Kun Bai,Kun Bai,Xuequn Shang,Guohua Wang,Hansheng Xue,Shuilin Jin,Liang Cheng,Yadong Wang,Jin Chen,Jin Chen +10 more
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.
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A novel method to measure the semantic similarity of HPO terms
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.
Posted Content
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
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.
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Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO
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.