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691 results on '"Zheng, Chun-Hou"'

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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3. A Retrieval-Based Molecular Style Transformation Optimization Model

Author

Wang, Cheng, Zhang, Ya-Jie, Xia, Xin, Su, Yan-sen, Zheng, Chun-hou, Wu, Qing-Wen, 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. Deep Hyper-Laplacian Regularized Self-representation Learning Based Structured Association Analysis for Brain Imaging Genetics

Author

Wang, Shuang-Qing, Jiao, Cui-Na, Wu, Tian-Ru, Cui, Xin-Chun, 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, Peng, Wei, editor, Cai, Zhipeng, editor, and Skums, Pavel, editor

Published
2024
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9. 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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10. Prediction of Cancer Driver Genes Based on Pyramidal Dynamic Mapping Algorithm

Author

Wei, Pi-Jing, Zhou, Shu-Li, Cao, Rui-Fen, Su, Yansen, 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, 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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11. Generative Adversarial Network-Based Data Augmentation Method for Anti-coronavirus Peptides Prediction

Author

Xu, Jiliang, Xu, Chungui, Cao, Ruifen, He, Yonghui, Bin, Yannan, 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, 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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14. 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

15. ARGLRR: An Adjusted Random Walk Graph Regularization Sparse Low-Rank Representation Method for Single-Cell RNA-Sequencing Data Clustering

Author

Wang, Zhen-Chang, Liu, Jin-Xing, Shang, Jun-Liang, Dai, Ling-Yun, Zheng, Chun-Hou, Wang, Juan, 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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16. A Unified Graph Attention Network Based Framework for Inferring circRNA-Disease Associations

Author

Ji, Cun-Mei, Liu, Zhi-Hao, Qiao, Li-Juan, Wang, Yu-Tian, 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, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Jo, Kang-Hyun, editor, Jing, Junfeng, editor, Premaratne, Prashan, editor, Bevilacqua, Vitoantonio, editor, and Hussain, Abir, editor

Published
2022
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17. Cell Classification Based on Stacked Autoencoder for Single-Cell RNA Sequencing

Author

Qi, Rong, Zheng, Chun-Hou, Ji, Cun-Mei, Yu, Ning, Ni, Jian-Cheng, Wang, Yu-Tian, 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, Jo, Kang-Hyun, editor, Jing, Junfeng, editor, Premaratne, Prashan, editor, Bevilacqua, Vitoantonio, editor, and Hussain, Abir, editor

Published
2022
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23. scMGATGRN: a multiview graph attention network–based method for inferring gene regulatory networks from single-cell transcriptomic data.

Author

Yuan, Lin, Zhao, Ling, Jiang, Yufeng, Shen, Zhen, Zhang, Qinhu, Zhang, Ming, Zheng, Chun-Hou, and Huang, De-Shuang

Subjects
MACHINE learning, DEEP learning, RNA sequencing, CELL lines, CELL anatomy
Abstract

The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)–based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN). [ABSTRACT FROM AUTHOR]

Published
2024
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27. 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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28. RWRNCP: Random Walking with Restart Based Network Consistency Projection for Predicting miRNA-Disease Association

Author

Zhang, Ming-Wen, Wang, Yu-Tian, Gao, Zhen, Li, Lei, Ni, Jian-Cheng, 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, Jo, Kang-Hyun, editor, Li, Jianqiang, editor, Gribova, Valeriya, editor, and Premaratne, Prashan, editor

Published
2021
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29. MELPMDA: A New Method Based on Matrix Enhancement and Label Propagation for Predicting miRNA-Disease Association

Author

Zhang, Zhen-Wei, Gao, Zhen, Zheng, Chun-Hou, Wang, Yu-Tian, Qi, Su-Min, 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 Premaratne, Prashan, editor

Published
2021
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30. SHDC: A Method of Similarity Measurement Using Heat Kernel Based on Denoising for Clustering scRNA-seq Data

Author

Zhao, Jian-ping, Wang, Hai-yun, 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, Jo, Kang-Hyun, editor, Li, Jianqiang, editor, Gribova, Valeriya, editor, and Premaratne, Prashan, editor

Published
2021
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32. Enhancing Spatial Domain Identification in Spatially Resolved Transcriptomics Using Graph Convolutional Networks With Adaptively Feature-Spatial Balance and Contrastive Learning

Author

Liang, Xuena, Shang, Junliang, Liu, Jin-Xing, Zheng, Chun-Hou, and Wang, Juan

Abstract

Recent advancements in spatially transcriptomics (ST) technologies have enabled the comprehensive measurement of gene expression profiles while preserving the spatial information of cells. Combining gene expression profiles and spatial information has been the most commonly used method to identify spatial functional domains and genes. However, most existing spatial domain decipherer methods are more focused on spatially neighboring structures and fail to take into account balancing the self-characteristics and the spatial structure dependency of spots. Therefore, we propose a novel model called SpaGCAC, which recognizes spatial domains with the help of an adaptive feature-spatial balanced graph convolutional network named AFSBGCN. The AFSBGCN can dynamically learn the relationship between spatial local topology structures and the self-characteristics of spots by adaptively increasing or declining the weight on the self-characteristics during message aggregation. Moreover, to better capture the local structures of spots, SpaGCAC exploits a local topology structure contrastive learning strategy. Meanwhile, SpaGCAC utilizes a probability distribution contrastive learning strategy to increase the similarity of probability distributions for points belonging to the same category. We validate the performance of SpaGCAC for spatial domain identification on four spatial transcriptomic datasets. In comparison with seven spatial domain recognition methods, SpaGCAC achieved the highest NMI median of 0.683 and the second highest ARI median of 0.559 on the multi-slice DLPFC dataset. SpaGCAC achieved the best results on all three other single-slice datasets. The above-mentioned results show that SpaGCAC outperforms most existing methods, providing enhanced insights into tissue heterogeneity.

Published
2024
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33. A New Graph Autoencoder-Based Multi-Level Kernel Subspace Fusion Framework for Single-Cell Type Identification

Author

Wang, Juan, Qiao, Tian-Jing, Zheng, Chun-Hou, Liu, Jin-Xing, and Shang, Jun-Liang

Abstract

The advent of single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to conduct biological research at the cellular level. Single-cell type identification based on unsupervised clustering is one of the fundamental tasks of scRNA-seq data analysis. Although many single-cell clustering methods have been developed recently, few can fully exploit the deep potential relationships between cells, resulting in suboptimal clustering. In this paper, we propose scGAMF, a graph autoencoder-based multi-level kernel subspace fusion framework for scRNA-seq data analysis. Based on multiple top feature sets, scGAMF unifies deep feature embedding and kernel space analysis into a single framework to learn an accurate clustering affinity matrix. First, we construct multiple top feature sets to avoid the high variability caused by single feature set learning. Second, scGAMF uses a graph autoencoder (GAEs) to extract deep information embedded in the data, and learn embeddings including gene expression patterns and cell-cell relationships. Third, to fully explore the deep potential relationships between cells, we design a multi-level kernel space fusion strategy. This strategy uses a kernel expression model with adaptive similarity preservation to learn a self-expression matrix shared by all embedding spaces of a given feature set, and a consensus affinity matrix across multiple top feature sets. Finally, the consensus affinity matrix is used for spectral clustering, visualization, and identification of gene markers. Extensive validation on real datasets shows that scGAMF achieves higher clustering accuracy than many popular single-cell analysis methods.

Published
2024
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37. 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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38. A Novel Clustering-Framework of Gene Expression Data Based on the Combination Between Deep Learning and Self-organizing Map

Author

Cui, Yan, Gao, Huacheng, Zhang, Rui, Lu, Yuanyuan, Xue, Yuan, 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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39. Discovery of Cancer Subtypes Based on Stacked Autoencoder

Author

Zhang, Bo, Cao, Rui-Fen, Wang, Jing, 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 Premaratne, Prashan, editor

Published
2020
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40. iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.

Author

Yuan, Lin, Zhao, Ling, Lai, Jinling, Jiang, Yufeng, Zhang, Qinhu, Shen, Zhen, Zheng, Chun-Hou, and Huang, De-Shuang

Subjects
CONVOLUTIONAL neural networks, NUCLEOTIDE sequence, MACHINE learning, MOLECULAR biology, CYTOLOGY
Abstract

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites. Author summary: The interaction between circRNAs and RBPs is one of the main activities of circRNAs. CircRNAs participate in the occurrence and development of diseases by interacting with RBPs. Identifying circRNA-RBP interaction sites have become a fundamental step for exploring the role of circRNA in the occurrence and progression of diseases. Many computational methods have been proposed to predict circRNA-RBP interaction sites. Nevertheless, they still have several limitations. For long nucleotide sequence data of circRNA, traditional CNN or LSTM cannot effectively capture long-distance dependencies (relationships between non-adjacent nucleotides in a circRNA). Furthermore, existing methods fail to effectively utilize the interaction information of multiple features, and insufficient consideration of interaction information leads to biased circRNA-RBP interaction relationships. To overcome these limitations, we propose iCRBP-LKHA, based on a large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites. We compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. Experimental results not only show that iCRBP-LKHA outperforms competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites. [ABSTRACT FROM AUTHOR]

Published
2024
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41. A Link and Weight-Based Ensemble Clustering for Patient Stratification

Author

Zhang, Yuan-Yuan, Yang, Chao, Wang, Jing, 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, Jo, Kang-Hyun, editor, and Huang, Zhi-Kai, editor

Published
2019
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42. Discovering Driver Mutation Profiles in Cancer with a Local Centrality Score

Author

Hui, Ying, Wei, Pi-Jing, Xia, Jun-Feng, Wang, Hong-Bo, Wang, Jing, 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, Jo, Kang-Hyun, editor, and Huang, Zhi-Kai, editor

Published
2019
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43. HGMDA: HyperGraph for Predicting MiRNA-Disease Association

Author

Wu, Qing-Wen, Wang, Yu-Tian, Gao, Zhen, Zhang, Ming-Wen, Ni, Jian-Cheng, 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, Jo, Kang-Hyun, editor, and Huang, Zhi-Kai, editor

Published
2019
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44. Improved Inductive Matrix Completion Method for Predicting MicroRNA-Disease Associations

Author

Ding, Xin, Xia, Jun-Feng, Wang, Yu-Tian, Wang, Jing, 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, Jo, Kang-Hyun, editor, and Huang, Zhi-Kai, editor

Published
2019
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50. Inference of gene regulatory networks based on directed graph convolutional networks.

Author

Wei, Pi-Jing, Guo, Ziqiang, Gao, Zhen, Ding, Zheng, Cao, Rui-Fen, Su, Yansen, and Zheng, Chun-Hou

Subjects
GRAPH neural networks, DIRECTED graphs, CONVOLUTIONAL neural networks, GENE regulatory networks, FEATURE extraction, SYSTEMS biology, COMPUTATIONAL neuroscience
Abstract

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli , sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model. [ABSTRACT FROM AUTHOR]

Published
2024
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