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Your search keyword '"Gao, Ying-Lian"' showing total 298 results
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298 results on '"Gao, Ying-Lian"'

1. Heterogeneous network and graph attention auto-encoder for LncRNA-disease association prediction

Author

Liu, Jin-Xing, Xi, Wen-Yu, Dai, Ling-Yun, Zheng, Chun-Hou, and Gao, Ying-Lian

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods, I.2.4, I.2.6, I.2.m
Abstract

The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple sources of biomedical data are fully utilized to construct characteristics of lncRNAs and diseases, and linear and nonlinear characteristics are effectively integrated. Furthermore, a novel deep learning model based on graph attention automatic encoder is proposed, called HGATELDA. To begin with, the linear characteristics of lncRNAs and diseases are created by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix. Following this, the nonlinear features of diseases and lncRNAs are extracted using a graph attention auto-encoder, which largely retains the critical information and effectively aggregates the neighborhood information of nodes. In the end, LDAs can be predicted by fusing the linear and nonlinear characteristics of diseases and lncRNA. The HGATELDA model achieves an impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation indicating its superior performance in comparison to several recent prediction models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is further demonstrated by case studies. the HGATELDA model appears to be a viable computational model for predicting LDAs., Comment: 10 pages, 8 figures

Published
2024

2. stMCFN: A Multi-view Contrastive Fusion Method for Spatial Domain Identification in Spatial Transcriptomics

Author

Jing, Jing, Gao, Ying-Lian, Gao, Yue, Ge, Dao-Hui, Zheng, Chun-Hou, Liu, Jin-Xing, 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, Zhang, Qinhu, editor, and Guo, Jiayang, editor

Published
2024
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4. Identify Complex Higher-Order Associations Between Alzheimer’s Disease Genes and Imaging Markers Through Improved Adaptive Sparse Multi-view Canonical Correlation Analysis

Author

Wang, Yi-Ming, Kong, Xiang-Zhen, Guan, Bo-Xin, Zheng, Chun-Hou, Gao, Ying-Lian, 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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5. Spatial Domain Identification Based on Graph Attention Denoising Auto-encoder

Author

Gao, Yue, Zhang, Dai-Jun, Jiao, Cui-Na, Gao, Ying-Lian, Liu, Jin-Xing, 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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6. LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction

Author

Dou, Xu-Ran, Xi, Wen-Yu, Wu, Tian-Ru, Jiao, Cui-Na, Liu, Jin-Xing, Gao, Ying-Lian, 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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9. Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data

Author

Feng, Chun-Mei, Xu, Yong, Liu, Jin-Xing, Gao, Ying-Lian, and Zheng, Chun-Hou

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Principal Component Analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods; moreover, the class ambiguity within the sample is high. To overcome this problem, this study developed a new PCA method, which is named the Supervised Discriminative Sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed and its convergence proof is provided. The SDSPCA has been applied to common characteristic gene selection (com-characteristic gene) and tumor classification on multi-view biological data. The sparsity and classification performance of the SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods., Comment: This paper has been published on TNNLS

Published
2019

11. A Locality-Constrained Linear Coding-Based Ensemble Learning Framework for Predicting Potentially Disease-Associated MiRNAs

Author

Shen, Yi, Gao, Ying-Lian, Li, Shu-Zhen, Guan, Boxin, Liu, Jin-Xing, 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, Bansal, Mukul S., editor, Cai, Zhipeng, editor, and Mangul, Serghei, editor

Published
2022
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12. MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction

Author

Wang, Ying, Gao, Ying-Lian, Wang, Juan, Shang, Junliang, Liu, Jin-Xing, 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, Bansal, Mukul S., editor, Cai, Zhipeng, editor, and Mangul, Serghei, editor

Published
2022
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15. MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label Propagation

Author

Gao, Ying-Lian, Yin, Meng-Meng, Liu, Jin-Xing, Shang, Junliang, Zheng, Chun-Hou, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wei, Yanjie, editor, Li, Min, editor, Skums, Pavel, editor, and Cai, Zhipeng, editor

Published
2021
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16. Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification

Author

Niu, Zhen-Xin, Ren, Liang-Rui, Zhu, Rong, Kong, Xiang-Zhen, Gao, Ying-Lian, Liu, Jin-Xing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Jo, Kang-Hyun, editor, Li, Jianqiang, editor, Gribova, Valeriya, editor, and Hussain, Abir, editor

Published
2021
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20. DSNPCMF: Predicting MiRNA-Disease Associations with Collaborative Matrix Factorization Based on Double Sparse and Nearest Profile

Author

Yin, Meng-Meng, Cui, Zhen, Liu, Jin-Xing, Gao, Ying-Lian, Kong, Xiang-Zhen, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Han, Henry, editor, Wei, Tie, editor, Liu, Wenbin, editor, and Han, Fei, editor

Published
2020
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21. Robust Graph Regularized Extreme Learning Machine Auto Encoder and Its Application to Single-Cell Samples Classification

Author

Ren, Liang-Rui, Liu, Jin-Xing, Gao, Ying-Lian, Kong, Xiang-Zhen, Zheng, Chun-Hou, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, and Jo, Kang-Hyun, editor

Published
2020
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25. Dual Sparse Collaborative Matrix Factorization Method Based on Gaussian Kernel Function for Predicting LncRNA-Disease Associations

Author

Gao, Ming-Ming, Cui, Zhen, Gao, Ying-Lian, Li, Feng, Liu, Jin-Xing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Huang, Zhi-Kai, editor, and Hussain, Abir, editor

Published
2019
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32. Performance Analysis of Non-negative Matrix Factorization Methods on TCGA Data

Author

Hou, Mi-Xiao, Liu, Jin-Xing, Shang, Junliang, Gao, Ying-Lian, Kong, Xiang-Zhen, Dai, Ling-Yun, 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, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Huang, De-Shuang, editor, Jo, Kang-Hyun, editor, and Zhang, Xiao-Long, editor

Published
2018
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41. A Simple Review of Sparse Principal Components Analysis

Author

Feng, Chun-Mei, Gao, Ying-Lian, Liu, Jin-Xing, Zheng, Chun-Hou, Li, Sheng-Jun, Wang, Dong, 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, De-Shuang, editor, and Jo, Kang-Hyun, editor

Published
2016
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42. Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data

Author

Hou, Mi-Xiao, Gao, Ying-Lian, Liu, Jin-Xing, Shang, Jun-Liang, Zheng, Chun-Hou, 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, De-Shuang, editor, and Jo, Kang-Hyun, editor

Published
2016
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43. Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction

Author

Jiao, Cui-Na, Zhou, Feng, Liu, Bao-Min, Zheng, Chun-Hou, Liu, Jin-Xing, and Gao, Ying-Lian

Abstract

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases. Nevertheless, traditional experimental verification of MDAs is laborious and limited to small-scale. Therefore, it is necessary to develop reliable and effective computational methods to predict novel MDAs. In this work, a multi-kernel graph attention deep autoencoder (MGADAE) method is proposed to predict potential MDAs. In detail, MGADAE first employs the multiple kernel learning (MKL) algorithm to construct an integrated miRNA similarity and disease similarity, providing more biological information for further feature learning. Second, MGADAE combines the known MDAs, disease similarity, and miRNA similarity into a heterogeneous network, then learns the representations of miRNAs and diseases through graph convolution operation. After that, an attention mechanism is introduced into MGADAE to integrate the representations from multiple graph convolutional network (GCN) layers. Lastly, the integrated representations of miRNAs and diseases are input into the bilinear decoder to obtain the final predicted association scores. Corresponding experiments prove that the proposed method outperforms existing advanced approaches in MDA prediction. Furthermore, case studies related to two human cancers provide further confirmation of the reliability of MGADAE in practice.

Published
2024
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44. A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection

Author

Zhang, Dai-Jun, Gao, Ying-Lian, Zhao, Jing-Xiu, Zheng, Chun-Hou, and Liu, Jin-Xing

Abstract

Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help capture cellular heterogeneity. This characteristic has advanced the field of genomics by enabling the delicate differentiation of cell types. However, the properties of single-cell datasets, such as high dropout events, noise, and high dimensionality, are still a research challenge in the single-cell field. To utilize single-cell data more efficiently and to better explore the heterogeneity among cells, a new graph autoencoder (GAE)-based consensus-guided model (scGAC) is proposed in this article. The data are preprocessed into multiple top-level feature datasets. Then, feature learning is performed by using GAEs to generate new feature matrices, followed by similarity learning based on distance fusion methods. The learned similarity matrices are fed back to the GAEs to guide their feature learning process. Finally, the abovementioned steps are iterated continuously to integrate the final consistent similarity matrix and perform other related downstream analyses. The scGAC model can accurately identify critical features and effectively preserve the internal structure of the data. This can further improve the accuracy of cell type identification.

Published
2024
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45. KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder

Author

Kang, Wen-Yue, Gao, Ying-Lian, Wang, Ying, Li, Feng, and Liu, Jin-Xing

Abstract

CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes. To solve these problems, this paper proposes a novel method based on Kernel Fusion and Deep Auto-encoder (KFDAE) to predict the potential associations between circRNAs and diseases. Firstly, KFDAE uses a non-linear method to fuse the circRNA similarity kernels and disease similarity kernels. Then the vectors are connected to make the positive and negative sample sets, and these data are send to deep auto-encoder to reduce dimension and extract features. Finally, three-layer deep feedforward neural network is used to learn features and gain the prediction score. The experimental results show that compared with existing methods, KFDAE achieves the best performance. In addition, the results of case studies prove the effectiveness and practical significance of KFDAE, which means KFDAE is able to capture more comprehensive information and generate credible candidate for subsequent wet-lab.

Published
2024
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