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Your search keyword '"Liao, Hongmei"' showing total 231 results
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231 results on '"Liao, Hongmei"'

3. Lightweight Coal Flow Foreign Object Detection Algorithm

Author

Nie, Ru, Shen, Xiaobing, Li, Zhengwei, Jiang, Yanxia, Liao, Hongmei, You, Zhuhong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Si, Zhanjun, editor, and Pan, Yijie, editor

Published
2024
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12. Towards Faster Deep Graph Clustering via Efficient Graph Auto-Encoder.

Author

Ding, Shifei, Wu, Benyu, Ding, Ling, Xu, Xiao, Guo, Lili, Liao, Hongmei, and Wu, Xindong

Subjects
CONVOLUTIONAL neural networks, GRAPH neural networks, COMPUTATIONAL complexity
Abstract

Deep graph clustering (DGC) has been a promising method for clustering graph data in recent years. However, existing research primarily focuses on optimizing clustering outcomes by improving the quality of embedded representations, resulting in slow-speed complex models. Additionally, these methods do not consider changes in node similarity and corresponding adjustments in the original structure during the iterative optimization process after updating node embeddings, which easily falls into the representation collapse issue. We introduce an Efficient Graph Auto-Encoder (EGAE) and a dynamic graph weight updating strategy to address these issues, forming the basis for our proposed Fast DGC (FastDGC) network. Specifically, we significantly reduce feature dimensions using a linear transformation that preserves the original node similarity. We then employ a single-layer graph convolutional filtering approximation to replace multiple layers of graph convolutional neural network, reducing computational complexity and parameter count. During iteration, we calculate the similarity between nodes using the linearly transformed features and periodically update the original graph structure to reduce edges with low similarity, thereby enhancing the learning of discriminative and cohesive representations. Theoretical analysis confirms that EGAE has lower computational complexity. Extensive experiments on standard datasets demonstrate that our proposed method improves clustering performance and achieves a speedup of 2–3 orders of magnitude compared to state-of-the-art methods, showcasing outstanding performance. The code for our model is available at https://github.com/Marigoldwu/FastDGC. Furthermore, we have organized a portion of the DGC code into a unified framework, available at https://github.com/Marigoldwu/A-Unified-Framework-for-Deep-Attribute-Graph-Clustering. [ABSTRACT FROM AUTHOR]

Published
2024
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27. HCN1pathogenic variants associated with childhood epilepsy in a cohort of Chinese patients

Author

Yang, Zhuanyi, Kuang, Zhuo, Liao, Hongmei, Gan, Siyi, Peng, Xiaomei, Yang, Haiyan, and Wu, Liwen

Abstract

HCN ion channel family has a widespread expression in neurons, and recently, increasing studies have demonstrated their roles in epilepsies. Clinical data of the patients were gathered in a retrospective study. Exon sequencing was used for the patients with unexplained recurrent seizures and varying levels of developmental delay. In this study, eight de novo variants of HCN1genes were uncovered in eight patients, including six missense variants, one nonsense variant and one frameshift insertion variant; five of them were reported for the first time. The onset age for eight patients ranges from one month to one year. Their main clinical manifestations are epilepsy and varying degrees of developmental delay, and the main type of seizure is focal secondary generalized tonic–clonic seizure. Importantly, in our study, one case presented with a form of migrating focal seizure that has not been reported in the literature. Seizures from five of the eight children were effectively controlled with antiepileptic drugs including valproic acid, levetiracetam and oxcarbazepine. One child developed normally and four children developed mild delay. One child was treated with topiramate, and the convulsion was partially controlled and showed moderate to severe developmental delay. The antiepileptic treatment failed for the other two children, and the two children were treated with sodium valproate, oxcarbazepine, lamotrigine, chlorbazan, levetiracetam and nitrodiazepam successively, but their convulsions were not controlled and showed moderate to severe developmental delay. Our research reported eight variants in HCN1gene causing epilepsy; among these variants, five variants were never reported before. HCN1‐related epilepsy usually starts infantile period, and focal secondary generalized tonic–clonic seizure is the most common seizure type. Importantly, we reported the case with migrating focal seizure was rarely reported. Our study expanded both genotype and phenotype for HCN1‐related epilepsy.

Published
2024
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31. LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations

Author

Wang, Wenjing, Han, Pengyong, Li, Zhengwei, Nie, Ru, Wang, Kangwei, Wang, Lei, and Liao, Hongmei

Abstract

Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this article, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA). In particular, LMGATCDA first incorporates information on circRNA functional similarity, disease semantic similarity, and the Gaussian interaction profile (GIP) kernel similarity as explicit features, along with node-labeling of the three-hop subgraphs extracted from each linked target node as graph structural features. After that, the fused features are used as input, and further implied features are extracted by graph sampling aggregation (GraphSAGE) and multi-hop attention graph neural network (MAGNA). Finally, the prediction scores are obtained through a fully connected layer. With five-fold cross-validation, LMGATCDA demonstrated excellent competitiveness against gold standard data, reaching 95.37% accuracy and 91.31% recall with an AUC of 94.25% on the circR2Disease benchmark dataset. Collectively, the noteworthy findings from these case studies support our conclusion that the LMGATCDA model can provide reliable circRNA-disease associations for clinical research while helping to mitigate experimental uncertainties in wet-lab investigations.

Published
2024
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33. A Variable Weight Based Fuzzy Data Fusion Algorithm for WSN

Author

Wang, Qianping, Liao, Hongmei, Wang, Ke, Sang, Yuan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Hsu, Ching-Hsien, editor, Yang, Laurence T., editor, Ma, Jianhua, editor, and Zhu, Chunsheng, editor

Published
2011
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42. Relationship between peripheral blood eosinophil count and the severity and prognosis of children with Duchenne Muscular Dystrophy : a new potentially useful predictor?

Author

Jiang, Zhi, primary, Liao, Hongmei, additional, Wu, Liwen, additional, Hu, Wenjing, additional, Yang, Liming, additional, Chen, Bo, additional, Ning, Zeshu, additional, Tang, Jingwen, additional, Xu, Rong, additional, Chen, Mei, additional, Guo, Feng, additional, and Liu, Shulei, additional

Published
2022
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