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Your search keyword '"Geng, Yushui"' showing total 99 results
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99 results on '"Geng, Yushui"'

1. Cluster Analysis of Scrna-Seq Data Combining Bioinformatics with Graph Attention Autoencoders and Ensemble Clustering

Author

Yuan, Lin, Xu, Zhijie, Li, Zhujun, Zhang, Shoukang, Hu, Chunyu, Yu, Wendong, Wei, Hongwei, Wang, Xingang, Geng, Yushui, 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, Pan, Yijie, editor, and Zhang, Qinhu, editor

Published
2024
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2. ScADSATGRN: An Adaptive Diffusion Structure-Aware Transformer Based Method Inferring Gene Regulatory Networks from Single-Cell Transcriptomic Data

Author

Yuan, Lin, Zhao, Ling, Li, Zhujun, Hu, Chunyu, Zhang, Shoukang, Wang, Xingang, Geng, Yushui, 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, Pan, Yijie, editor, and Zhang, Qinhu, editor

Published
2024
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3. Adaptive Weight Sampling and Graph Transformer Neural Network Framework for Cell Type Annotation of Scrna-seq Data

Author

Yuan, Lin, Sun, Shengguo, Li, Zhujun, Zhang, Shoukang, Wang, Xingang, Geng, Yushui, 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, Pan, Yijie, editor, and Zhang, Qinhu, editor

Published
2024
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7. An Improved Method for CFNet Identifying Glioma Cells

Author

Yuan, Lin, Lai, Jinling, Shen, Zhen, Yu, Wendong, Wei, Hongwei, Zhao, Ling, Xu, Zhijie, Wang, Xingang, Geng, Yushui, Goos, Gerhard, Founding 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, Premaratne, Prashan, editor, Jin, Baohua, editor, Qu, Boyang, editor, Jo, Kang-Hyun, editor, and Hussain, Abir, editor

Published
2023
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8. Identification of CircRNA-Disease Associations from the Integration of Multi-dimensional Bioinformatics with Graph Auto-encoder and Attention Fusion Model

Author

Yuan, Lin, Zhao, Jiawang, Shen, Zhen, Yu, Wendong, Wei, Hongwei, Sun, Shengguo, Wang, Xingang, Geng, Yushui, Goos, Gerhard, Founding 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, Premaratne, Prashan, editor, Jin, Baohua, editor, Qu, Boyang, editor, Jo, Kang-Hyun, editor, and Hussain, Abir, editor

Published
2023
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10. An Improved Attribute Value-Weighted Double-Layer Hidden Naive Bayes Classification Algorithm

Author

Zhang, Huanying, Geng, Yushui, Wang, Fei, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Liu, Qi, editor, Liu, Xiaodong, editor, Li, Lang, editor, Zhou, Huiyu, editor, and Zhao, Hui-Huang, editor

Published
2021
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11. An Improved Fuzzy C-Means Clustering Algorithm Based on Intuitionistic Fuzzy Sets

Author

Wang, Fei, Geng, Yushui, Zhang, Huanying, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Liu, Qi, editor, Liu, Xiaodong, editor, Li, Lang, editor, Zhou, Huiyu, editor, and Zhao, Hui-Huang, editor

Published
2021
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32. LncRNA-Disease Association Prediction Based on Graph Neural Networks and Inductive Matrix Completion

Author

Geng Yushui, Sun Tao, Zhen-Yu Yang, Jiang Xuesong, Jing Zhao, Lin Yuan, and Xin Gang Wang

Subjects
Matrix completion, business.industry, Computer science, Deep learning, Association (object-oriented programming), Linear model, Disease, Machine learning, computer.software_genre, Class (biology), Task (project management), Matrix (mathematics), Artificial intelligence, Representation (mathematics), business, computer
Abstract

Emerging evidence indicates that long non-coding RNA (lncRNA) plays a crucial role in human disease. Discovering disease-gene association is a fundamental and critical biomedical task, which assists biologists and physicians to discover complex pathogenic mechanisms under the phenotype. With high-throughput sequencing technology and various clinical biomarkers to measure the similarities between lncRNA and disease phenotype, network-based semi-supervised learning has been commonly utilized by these studies to address this class imbalanced large-scale data issue. However, most existing approaches are based on linear models and suffer from two major limitations: 1) They implicitly consider a local-structure representation for each candidate; 2) They are unable to capture nonlinear associations between lncRNAs and diseases. In this paper, we propose a new framework for lncRNA-disease association task by combining Graph Neural Network (GNN) and inductive matrix completion, named GNN-IMC. With the help of GNN, we could generate subgraphs based on (lncRNA, disease) pairs from the observed association matrix and maps these subgraphs to their corresponding associations. In addition, GNN-IMC is inductive–it can generalize to lncRNAs/diseases unseen during the training (given that their associations exist), and can even transfer to new tasks. Empirical results demonstrate that the proposed deep learning algorithm outperforms all other state-of-the-art methods on most of metrics.

Published
2020

33. A Network-Driven Approach for LncRNA-Disease Association Mapping

Author

Song Liu, Lu Qin, Li Aimin, Lin Yuan, Sun Tao, Xin Gang Wang, Geng Yushui, and Jing Zhao

Subjects
Disease status, Cancer genome, Association (object-oriented programming), medicine, Positive control, Cancer, Disease Association, Computational biology, Disease, Biology, medicine.disease, Association mapping
Abstract

Aberrant lncRNA may contributes to development of cancer. It remains, however, a challenge to understand associations between lncRNA and cancer because of complex mechanisms involved in associations and insufficient sample sizes. With unprecedented wealth of lncRNA data, gene expression data and disease status data give us a new opportunity to design machine learning method to investigate underlying association mechanisms. In this paper, we propose a network-driven approach named NLDAM which for lncRNA-disease association mapping. NLDAM detects associations between lncRNA and genes, genes and disease. NLDAM constructs an association network, where nodes represent lncRNA, genes or disease status, and weighted edges represent significance of associations between nodes. NLDAM identifies significant paths from lncRNA to disease based on the weighted scores. The experimental results on synthetic datasets show the advantage of NLDAM in terms of lncRNA selection accuracy than traditional methylation sites search methods (including NTSDMHN and IDHI-MIRW) under false positive control. Furthermore, we applied NLDAM on ovarian cancer data from The Cancer Genome Atlas database and identified significant lncRNA-gene-disease path associations, among which we analyzed top 10 paths associated with oncogenes. We also provide hypothetical biological path associations to explain our findings.

Published
2020

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