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Zhang T, Zhang SW, Xie MY, Li Y. Identifying cooperating cancer driver genes in individual patients through hypergraph random walk. J Biomed Inform 2024; 157:104710. [PMID: 39159864 DOI: 10.1016/j.jbi.2024.104710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024]
Abstract
OBJECTIVE Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Ming-Yu Xie
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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2
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Long S, Tang X, Si X, Kong T, Zhu Y, Wang C, Qi C, Mu Z, Liu J. TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network. Commun Biol 2024; 7:1067. [PMID: 39215090 PMCID: PMC11364641 DOI: 10.1038/s42003-024-06734-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024] Open
Abstract
The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge to accurately predict such associations due to improper information encoding. Previous methods characterize miRNA-disease associations only from single levels, causing the loss of multi-level association information. In this study, we propose TriFusion, a powerful and interpretable deep learning framework for miRNA-disease association prediction. It develops a tri-channel architecture to encode the association features of miRNAs and diseases from different levels and designs a feature fusion encoder to smoothly fuse these features. After training and testing, TriFusion outperforms other leading methods and offers strong interpretability through its learned representations. Furthermore, TriFusion is applied to three high-risk sexually associated cancers (ovarian, breast, and prostate cancers) and exhibits remarkable ability in the identification of miRNAs associated with the three diseases.
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Affiliation(s)
- Sheng Long
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Xiaoran Tang
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Xinyi Si
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Tongxin Kong
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Yanhao Zhu
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Chuanzhi Wang
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Chenqing Qi
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Zengchao Mu
- School of Mathematics and Statistics, Shandong University, Weihai, China.
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University, Weihai, China.
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3
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Wang T, Zhuo L, Chen Y, Fu X, Zeng X, Zou Q. ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification. PLoS Comput Biol 2024; 20:e1012400. [PMID: 39213450 PMCID: PMC11392234 DOI: 10.1371/journal.pcbi.1012400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 09/12/2024] [Accepted: 08/10/2024] [Indexed: 09/04/2024] Open
Abstract
The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.
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Affiliation(s)
- Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
| | - Yifan Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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4
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Yang J, Fu H, Xue F, Li M, Wu Y, Yu Z, Luo H, Gong J, Niu X, Zhang W. Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms. Brief Bioinform 2024; 25:bbae418. [PMID: 39210506 PMCID: PMC11361854 DOI: 10.1093/bib/bbae418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/08/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024] Open
Abstract
Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.
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Affiliation(s)
- Jianye Yang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Haitao Fu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- School of Artificial Intelligence, Hubei University, Wuhan 430070, China
| | - Feiyang Xue
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Menglu Li
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuyang Wu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhanhui Yu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Haohui Luo
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jing Gong
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430062, China
| | - Xiaohui Niu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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5
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Deng C, Li HD, Zhang LS, Liu Y, Li Y, Wang J. Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks. Bioinformatics 2024; 40:i511-i520. [PMID: 38940121 PMCID: PMC11211849 DOI: 10.1093/bioinformatics/btae257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. RESULTS Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. AVAILABILITY AND IMPLEMENTATION DISHyper is freely available for download at https://github.com/genemine/DISHyper.
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Affiliation(s)
- Chao Deng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Hong-Dong Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Li-Shen Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Yiwei Liu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529-0001, United States
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
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6
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Wang L, Zhou J, Wang X, Wang Y, Li J. MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis. Bioinformatics 2024; 40:btae362. [PMID: 38867699 PMCID: PMC11256962 DOI: 10.1093/bioinformatics/btae362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/09/2024] [Accepted: 06/11/2024] [Indexed: 06/14/2024] Open
Abstract
MOTIVATION Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. RESULTS MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. AVAILABILITY AND IMPLEMENTATION https://github.com/1160300611/MCDHGN.
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Affiliation(s)
- Lexiang Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Jingli Zhou
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Xuan Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Yadong Wang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
- Ministry of Education, Key Laboratory of Biological Bigdata, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Ministry of Education, Key Laboratory of Biological Bigdata, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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7
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Luo Y, Li S, Peng L, Ding P, Liang W. Predicting associations between drugs and G protein-coupled receptors using a multi-graph convolutional network. Comput Biol Chem 2024; 110:108060. [PMID: 38579550 DOI: 10.1016/j.compbiolchem.2024.108060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024]
Abstract
Developing new drugs is an expensive, time-consuming process that frequently involves safety concerns. By discovering novel uses for previously verified drugs, drug repurposing helps to bypass the time-consuming and costly process of drug development. As the largest family of proteins targeted by verified drugs, G protein-coupled receptors (GPCR) are vital to efficiently repurpose drugs by inferring their associations with drugs. Drug repurposing may be sped up by computational models that predict the strength of novel drug-GPCR pairs interaction. To this end, a number of models have been put forth. In existing methods, however, drug structure, drug-drug interactions, GPCR sequence, and subfamily information couldn't simultaneously be taken into account to detect novel drugs-GPCR relationships. In this study, based on a multi-graph convolutional network, an end-to-end deep model was developed to efficiently and precisely discover latent drug-GPCR relationships by combining data from multi-sources. We demonstrated that our model, based on multi-graph convolutional networks, outperformed rival deep learning techniques as well as non-deep learning models in terms of inferring drug-GPCR relationships. Our results indicated that integrating data from multi-sources can lead to further advancement.
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Affiliation(s)
- Yuxun Luo
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
| | - Shasha Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China.
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
| | - Wei Liang
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China.
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8
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Ma X, Li Z, Du Z, Xu Y, Chen Y, Zhuo L, Fu X, Liu R. Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction. Comput Biol Med 2024; 174:108484. [PMID: 38643595 DOI: 10.1016/j.compbiomed.2024.108484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/18/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.
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Affiliation(s)
- Xinqian Ma
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China
| | - Zhen Li
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China; Institute of Computational Science and Technology, Guangzhou University, 510000, Guangzhou, China
| | - Zhenya Du
- Guangzhou Xinhua University, 510520, Guangzhou, China
| | - Yan Xu
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China
| | - Yifan Chen
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China.
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012, Changsha, China
| | - Ruijun Liu
- School of Software, Beihang University, Beijing, China.
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9
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Niu R, Guo Y, Shang X. GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network. iScience 2024; 27:109387. [PMID: 38510118 PMCID: PMC10951990 DOI: 10.1016/j.isci.2024.109387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
Identifying cancer genes is vital for cancer diagnosis and treatment. However, because of the complexity of cancer occurrence and limited cancer genes knowledge, it is hard to identify cancer genes accurately using only a few omics data, and the overall performance of existing methods is being called for further improvement. Here, we introduce a two-stage gradual-learning strategy GLIMS to predict cancer genes using integrative features from multi-omics data. Firstly, it uses a semi-supervised hierarchical graph neural network to predict the initial candidate cancer genes by integrating multi-omics data and protein-protein interaction (PPI) network. Then, it uses an unsupervised approach to further optimize the initial prediction by integrating the co-splicing network in post-transcriptional regulation, which plays an important role in cancer development. Systematic experiments on multi-omics cancer data demonstrated that GLIMS outperforms the state-of-the-art methods for the identification of cancer genes and it could be a useful tool to help advance cancer analysis.
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Affiliation(s)
- Rui Niu
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yang Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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10
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Wei PJ, Zhu AD, Cao R, Zheng C. Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields. BIOLOGY 2024; 13:184. [PMID: 38534453 DOI: 10.3390/biology13030184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/03/2024] [Accepted: 03/10/2024] [Indexed: 03/28/2024]
Abstract
Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.
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Affiliation(s)
- Pi-Jing Wei
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - An-Dong Zhu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - Ruifen Cao
- School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - Chunhou Zheng
- School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Hefei 230601, China
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11
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Zheng T, Zheng Z, Zhou H, Guo Y, Li S. The multifaceted roles of COL4A4 in lung adenocarcinoma: An integrated bioinformatics and experimental study. Comput Biol Med 2024; 170:107896. [PMID: 38217972 DOI: 10.1016/j.compbiomed.2023.107896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/03/2023] [Accepted: 12/23/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND Abnormal expression of collagen IV subunits has been reported in cancers, but the significance is not clear. No study has reported the significance of COL4A4 in lung adenocarcinoma (LUAD). METHODS COL4A4 expression data, single-cell sequencing data and clinical data were downloaded from public databases. A range of bioinformatics and experimental methods were adopted to analyze the association of COL4A4 expression with clinical parameters, tumor microenvironment (TME), drug resistance and immunotherapy response, and to investigate the roles and underlying mechanism of COL4A4 in LUAD. RESULTS COL4A4 is differentially expressed in most of cancers analyzed, being associated with prognosis, tumor stemness, immune checkpoint gene expression and TME parameters. In LUAD, COL4A4 expression is down-regulated and associated with various TME parameters, response to immunotherapy and drug resistance. LUAD patients with lower COL4A4 have worse prognosis. Knockdown of COL4A4 significantly inhibited the expression of cell-cycle associated genes, and the expression and activation of signaling pathways including JAK/STAT3, p38, and ERK pathways, and induced quiescence in LUAD cells. Besides, it significantly induced the expression of a range of bioactive molecule genes that have been shown to have critical roles in TME remodeling and immune regulation. CONCLUSIONS COL4A4 is implicated in the pathogenesis of cancers including LUAD. Its function may be multifaceted. It can modulate the activity of LUAD cells, TME remodeling and tumor stemness, thus affecting the pathological process of LUAD. COL4A4 may be a prognostic molecular marker and a potential therapeutic target.
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Affiliation(s)
- Tiaozhan Zheng
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, Zhuang Autonomous Region, 530021, PR China
| | - Zhiwen Zheng
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, Zhuang Autonomous Region, 530021, PR China
| | - Hanxi Zhou
- Department of Pathology, Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, PR China
| | - Yiqing Guo
- Department of Pathology, Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, PR China
| | - Shikang Li
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, Zhuang Autonomous Region, 530021, PR China.
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12
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Xuan P, Gu J, Cui H, Wang S, Toshiya N, Liu C, Zhang T. Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes. Bioinformatics 2024; 40:btae025. [PMID: 38269610 PMCID: PMC10868329 DOI: 10.1093/bioinformatics/btae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/26/2023] [Accepted: 01/22/2024] [Indexed: 01/26/2024] Open
Abstract
MOTIVATION The human microbiome may impact the effectiveness of drugs by modulating their activities and toxicities. Predicting candidate microbes for drugs can facilitate the exploration of the therapeutic effects of drugs. Most recent methods concentrate on constructing of the prediction models based on graph reasoning. They fail to sufficiently exploit the topology and position information, the heterogeneity of multiple types of nodes and connections, and the long-distance correlations among nodes in microbe-drug heterogeneous graph. RESULTS We propose a new microbe-drug association prediction model, NGMDA, to encode the position and topological features of microbe (drug) nodes, and fuse the different types of features from neighbors and the whole heterogeneous graph. First, we formulate the position and topology features of microbe (drug) nodes by t-step random walks, and the features reveal the topological neighborhoods at multiple scales and the position of each node. Second, as the features of nodes are high-dimensional and sparse, we designed an embedding enhancement strategy based on supervised fully connected autoencoders to form the embeddings with representative features and the more discriminative node distributions. Third, we propose an adaptive neighbor feature fusion module, which fuses features of neighbors by the constructed position- and topology-sensitive heterogeneous graph neural networks. A novel self-attention mechanism is developed to estimate the importance of the position and topology of each neighbor to a target node. Finally, a heterogeneous graph feature fusion module is constructed to learn the long-distance correlations among the nodes in the whole heterogeneous graph by a relationship-aware graph transformer. Relationship-aware graph transformer contains the strategy for encoding the connection relationship types among the nodes, which is helpful for integrating the diverse semantics of these connections. The extensive comparison experimental results demonstrate NGMDA's superior performance over five state-of-the-art prediction methods. The ablation experiment shows the contributions of the multi-scale topology and position feature learning, the embedding enhancement strategy, the neighbor feature fusion, and the heterogeneous graph feature fusion. Case studies over three drugs further indicate that NGMDA has ability in discovering the potential drug-related microbes. AVAILABILITY AND IMPLEMENTATION Source codes and Supplementary Material are available at https://github.com/pingxuan-hlju/NGMDA.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Department of Computer Science, Shantou University, Shantou 515063, China
| | - Jing Gu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3083, Australia
| | - Shuai Wang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Nakaguchi Toshiya
- Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan
| | - Cheng Liu
- Department of Computer Science, Shantou University, Shantou 515063, China
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China
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13
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Liu H, Peng W, Dai W, Lin J, Fu X, Liu L, Liu L, Yu N. Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks. Methods 2024; 222:41-50. [PMID: 38157919 DOI: 10.1016/j.ymeth.2023.11.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/19/2023] [Accepted: 11/19/2023] [Indexed: 01/03/2024] Open
Abstract
Predicting the therapeutic effect of anti-cancer drugs on tumors based on the characteristics of tumors and patients is one of the important contents of precision oncology. Existing computational methods regard the drug response prediction problem as a classification or regression task. However, few of them consider leveraging the relationship between the two tasks. In this work, we propose a Multi-task Interaction Graph Convolutional Network (MTIGCN) for anti-cancer drug response prediction. MTIGCN first utilizes an graph convolutional network-based model to produce embeddings for both cell lines and drugs. After that, the model employs multi-task learning to predict anti-cancer drug response, which involves training the model on three different tasks simultaneously: the main task of the drug sensitive or resistant classification task and the two auxiliary tasks of regression prediction and similarity network reconstruction. By sharing parameters and optimizing the losses of different tasks simultaneously, MTIGCN enhances the feature representation and reduces overfitting. The results of the experiments on two in vitro datasets demonstrated that MTIGCN outperformed seven state-of-the-art baseline methods. Moreover, the well-trained model on the in vitro dataset GDSC exhibited good performance when applied to predict drug responses in in vivo datasets PDX and TCGA. The case study confirmed the model's ability to discover unknown drug responses in cell lines.
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Affiliation(s)
- Hancheng Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.
| | - Wei Dai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.
| | - Jiangzhen Lin
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China
| | - Xiaodong Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
| | - Li Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China.
| | - Lijun Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
| | - Ning Yu
- State University of New York, The College at Brockport, Department of Computing Sciences, 350 New Campus Drive, Brockport NY 14422.
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14
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Chang Z, Zhu R, Liu J, Shang J, Dai L. HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss. Noncoding RNA 2024; 10:9. [PMID: 38392964 PMCID: PMC10893088 DOI: 10.3390/ncrna10010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024] Open
Abstract
Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.
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Affiliation(s)
| | - Rong Zhu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China; (Z.C.); (J.L.); (J.S.); (L.D.)
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15
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Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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16
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Li X, Li Y, Shang X, Kong H. A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus. Front Microbiol 2024; 15:1345794. [PMID: 38314434 PMCID: PMC10834737 DOI: 10.3389/fmicb.2024.1345794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024] Open
Abstract
Introduction Seasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features. Methods Here, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences. Results This method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022. Discussion Interestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.
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Affiliation(s)
- Xingyi Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- Big Data Storage and Management MIIT Lab, Xi'an, Shaanxi, China
| | - Yanyan Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- Big Data Storage and Management MIIT Lab, Xi'an, Shaanxi, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- Big Data Storage and Management MIIT Lab, Xi'an, Shaanxi, China
| | - Huihui Kong
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Harbin, China
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17
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Zhao BW, Su XR, Yang Y, Li DX, Li GD, Hu PW, Zhao YG, Hu L. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks. Methods 2023; 220:106-114. [PMID: 37972913 DOI: 10.1016/j.ymeth.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/13/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yue Yang
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Dong-Xu Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Guo-Dong Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yong-Gang Zhao
- Department of Orthopaedic Surgery (hand and foot trauma), People's Hospital of Dongxihu, Wuhan 420100, China.
| | - Lun Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
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18
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Peng W, Yu P, Dai W, Fu X, Liu L, Pan Y. A Graph Convolution Network-Based Model for Prioritizing Personalized Cancer Driver Genes of Individual Patients. IEEE Trans Nanobioscience 2023; 22:744-754. [PMID: 37195839 DOI: 10.1109/tnb.2023.3277316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Cancer driver genes are mutated genes that play a key role in the growth of cancer cells. Accurately identifying the cancer driver genes helps us understand cancer's pathogenesis and develop effective treatment strategies. However, cancers are highly heterogeneous diseases; patients with the same cancer type may have different genomic characteristics and clinical symptoms. Hence, it is urgent to devise effective methods to identify personalized cancer driver genes of individual patients to help determine whether a patient can be treated with a certain targeted drug. This work presents a method for predicting personalized cancer Driver genes of individual patients based on Graph Convolution Networks and Neighbor Interactions called NIGCNDriver. NIGCNDriver first constructs a gene-sample association matrix using the associations between a sample and its known driver genes. Then, it employs graph convolution models on the gene-sample network to aggregate neighbor node features, and themself features, and then combines with the element-wise level interactions between neighbors to learn new feature representations for the samples and gene nodes. Finally, a linear correlation coefficient decoder is used to reconstruct the association between the sample and the mutant gene, enabling the prediction of a personalized driver gene for the individual sample. We applied the NIGCNDriver method to predict cancer driver genes for individual samples in the TCGA and cancer cell line datasets. The results show that our method outperforms the baseline methods in cancer driver gene prediction for individual samples.
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19
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Cui Y, Wang Z, Wang X, Zhang Y, Zhang Y, Pan T, Zhang Z, Li S, Guo Y, Akutsu T, Song J. SMG: self-supervised masked graph learning for cancer gene identification. Brief Bioinform 2023; 24:bbad406. [PMID: 37950905 PMCID: PMC10639095 DOI: 10.1093/bib/bbad406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/26/2023] [Accepted: 10/24/2023] [Indexed: 11/13/2023] Open
Abstract
Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein-protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.
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Affiliation(s)
- Yan Cui
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Zhikang Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Xiaoyu Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Yiwen Zhang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Ying Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Tong Pan
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | | | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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20
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Yang H, Liu Y, Yang Y, Li D, Wang Z. InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data. Brief Bioinform 2023; 24:bbad318. [PMID: 37649392 DOI: 10.1093/bib/bbad318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 06/14/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these genes is crucial in advancing our understanding of cancer pathogenesis and developing targeted cancer drugs. Despite the current methods for discovering cancer driver genes that mainly rely on integrating multi-omics data, many existing models are overly complex, and it is difficult to interpret the results accurately. This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multi-omics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes and refines the cancer driver gene catalog. In comparison with state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP's identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes. InDEP's ability to identify reliable candidate driver genes opens up new avenues for precision oncology and discovering new biomedical knowledge. This framework can help advance cancer research by providing an interpretable method for identifying cancer driver genes and their contribution to cancer pathogenesis, facilitating the development of targeted cancer drugs.
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Affiliation(s)
- Hai Yang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, PR China
| | - Yawen Liu
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, PR China
| | - Yijing Yang
- Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, PR China
| | - Zhe Wang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, PR China
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21
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Lee M. Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review. Molecules 2023; 28:5169. [PMID: 37446831 DOI: 10.3390/molecules28135169] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.
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Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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22
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Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
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Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
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23
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Wu J, Nie Q, Li G, Zhu K. Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm. BMC Bioinformatics 2023; 24:211. [PMID: 37221474 DOI: 10.1186/s12859-023-05319-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/04/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. RESULTS In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein-Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with "inclusion" relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the "inclusion" relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. CONCLUSIONS The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.
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Affiliation(s)
- Jingli Wu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China.
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China.
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China.
| | - Qinghua Nie
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China
| | - Gaoshi Li
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China
| | - Kai Zhu
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China
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Patterson A, Elbasir A, Tian B, Auslander N. Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications. Cancers (Basel) 2023; 15:1958. [PMID: 37046619 PMCID: PMC10093138 DOI: 10.3390/cancers15071958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.
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Affiliation(s)
- Andrew Patterson
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Wistar Institute, Philadelphia, PA 19104, USA
| | | | - Bin Tian
- The Wistar Institute, Philadelphia, PA 19104, USA
| | - Noam Auslander
- The Wistar Institute, Philadelphia, PA 19104, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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25
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Peng W, Chen T, Liu H, Dai W, Yu N, Lan W. Improving drug response prediction based on two-space graph convolution. Comput Biol Med 2023; 158:106859. [PMID: 37023539 DOI: 10.1016/j.compbiomed.2023.106859] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/22/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Patients with the same cancer types may present different genomic features and therefore have different drug sensitivities. Accordingly, correctly predicting patients' responses to the drugs can guide treatment decisions and improve the outcome of cancer patients. Existing computational methods leverage the graph convolution network model to aggregate features of different types of nodes in the heterogeneous network. They most fail to consider the similarity between homogeneous nodes. To this end, we propose an algorithm based on two-space graph convolutional neural networks, TSGCNN, to predict the response of anticancer drugs. TSGCNN first constructs the cell line feature space and the drug feature space and separately performs the graph convolution operation on the feature spaces to diffuse similarity information among homogeneous nodes. After that, we generate a heterogeneous network based on the known cell line and drug relationship and perform graph convolution operations on the heterogeneous network to collect the features of different types of nodes. Subsequently, the algorithm produces the final feature representations for cell lines and drugs by adding their self features, the feature space representations, and the heterogeneous space representations. Finally, we leverage the linear correlation coefficient decoder to reconstruct the cell line-drug correlation matrix for drug response prediction based on the final representations. We tested our model on the Cancer Drug Sensitivity Data (GDSC) and Cancer Cell Line Encyclopedia (CCLE) databases. The results indicate that TSGCNN shows excellent performance drug response prediction compared with other eight state-of-the-art methods.
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Affiliation(s)
- Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, 650050, China.
| | - Tielin Chen
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650050, China
| | - Hancheng Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650050, China
| | - Wei Dai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650050, China; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, 650050, China
| | - Ning Yu
- State University of New York, The College at Brockport, Department of Computing Sciences, 350 New Campus Drive, Brockport, NY 14422, United States of America
| | - Wei Lan
- School of Computer Electronic and Information, Guangxi University, Nanning, Guangxi 530004, China
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26
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Peng W, Wu R, Dai W, Yu N. Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism. BMC Bioinformatics 2023; 24:16. [PMID: 36639646 PMCID: PMC9838012 DOI: 10.1186/s12859-023-05140-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 01/06/2023] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Correctly identifying the driver genes that promote cell growth can significantly assist drug design, cancer diagnosis and treatment. The recent large-scale cancer genomics projects have revealed multi-omics data from thousands of cancer patients, which requires to design effective models to unlock the hidden knowledge within the valuable data and discover cancer drivers contributing to tumorigenesis. RESULTS In this work, we propose a graph convolution network-based method called MRNGCN that integrates multiple gene relationship networks to identify cancer driver genes. First, we constructed three gene relationship networks, including the gene-gene, gene-outlying gene and gene-miRNA networks. Then, genes learnt feature presentations from the three networks through three sharing-parameter heterogeneous graph convolution network (HGCN) models with the self-attention mechanism. After that, these gene features pass a convolution layer to generate fused features. Finally, we utilized the fused features and the original feature to optimize the model by minimizing the node and link prediction losses. Meanwhile, we combined the fused features, the original features and the three features learned from every network through a logistic regression model to predict cancer driver genes. CONCLUSIONS We applied the MRNGCN to predict pan-cancer and cancer type-specific driver genes. Experimental results show that our model performs well in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC) compared to state-of-the-art methods. Ablation experimental results show that our model successfully improved the cancer driver identification by integrating multiple gene relationship networks.
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Affiliation(s)
- Wei Peng
- grid.218292.20000 0000 8571 108XFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650050 China ,grid.218292.20000 0000 8571 108XComputer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, 650050 China
| | - Rong Wu
- grid.218292.20000 0000 8571 108XFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650050 China
| | - Wei Dai
- grid.218292.20000 0000 8571 108XFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650050 China ,grid.218292.20000 0000 8571 108XComputer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, 650050 China
| | - Ning Yu
- grid.264262.60000 0001 0725 9953Department of Computing Sciences, The College at Brockport, State University of New York, 350 New Campus Drive, Brockport, NY 14422 USA
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Yue Y, Liu Y, Hao L, Lei H, He S. Improving therapeutic synergy score predictions with adverse effects using multi-task heterogeneous network learning. Brief Bioinform 2022; 24:6958504. [PMID: 36562724 PMCID: PMC9851313 DOI: 10.1093/bib/bbac564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/31/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022] Open
Abstract
Drug combinations could trigger pharmacological therapeutic effects (TEs) and adverse effects (AEs). Many computational methods have been developed to predict TEs, e.g. the therapeutic synergy scores of anti-cancer drug combinations, or AEs from drug-drug interactions. However, most of the methods treated the AEs and TEs predictions as two separate tasks, ignoring the potential mechanistic commonalities shared between them. Based on previous clinical observations, we hypothesized that by learning the shared mechanistic commonalities between AEs and TEs, we could learn the underlying MoAs (mechanisms of actions) and ultimately improve the accuracy of TE predictions. To test our hypothesis, we formulated the TE prediction problem as a multi-task heterogeneous network learning problem that performed TE and AE learning tasks simultaneously. To solve this problem, we proposed Muthene (multi-task heterogeneous network embedding) and evaluated it on our collected drug-drug interaction dataset with both TEs and AEs indications. Our experimental results showed that, by including the AE prediction as an auxiliary task, Muthene generated more accurate TE predictions than standard single-task learning methods, which supports our hypothesis. Using a drug pair Vincristine-Dasatinib as a case study, we demonstrated that our method not only provides a novel way of TE predictions but also helps us gain a deeper understanding of the MoAs of drug combinations.
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Affiliation(s)
- Yang Yue
- School of Computer Science from the University of Birmingham, UK
| | - Yongxuan Liu
- State Key Laboratory of Agricultural Microbiology from Huazhong Agricultural University, China
| | - Luoying Hao
- School of Computer Science from the University of Birmingham, UK
| | | | - Shan He
- Corresponding author. S. He, Centre for Computational Biology, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. Tel.: +44-1214142775; Fax: +44-1214144281; E-mail:
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28
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Xu Y, Cui X, Zhang L, Zhao T, Wang Y. Metastasis-related gene identification by compound constrained NMF and a semisupervised cluster approach using pancancer multiomics features. Comput Biol Med 2022; 151:106263. [PMID: 36371902 DOI: 10.1016/j.compbiomed.2022.106263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/16/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
In recent years, with the gradual increase in pancancer-related research, more attention has been given to the field of pancancer metastasis. However, the molecular mechanism of pancancer metastasis is very unclear, and identification methods for pancancer metastasis-related genes are still lacking. In view of this research status, we developed a novel pipeline to identify pancancer metastasis-related genes based on compound constrained nonnegative matrix factorization (CCNMF). To solve the above problems, the following modules were designed. A correntropy operator and feature similarity fusion (FSF) were first adopted to process the multiomics features of genes; thus, the influences caused by irrelevant biomolecular patterns, manifested as non-Gaussian noise, were minimized. CCNMF was then adopted to handle the above features with compound constraints consisting of a gene relation network and a "metastasis-related" gene set, which maximizes the biological interpretability of the metafeatures generated by NMF. Since a negative set of pancancer "metastasis-related" genes could hardly be obtained, semisupervised analyses were performed on gene features acquired by each step in our pipeline to examine our method's effect. 83% of the 236 candidates identified by the above method were associated with the metastasis of one or more cancers, 71.9% candidates were identified immune-related in pancancer in addition to the hallmark genes. Our study provides an effective and interpretable method for identifying metastasis-related as well as immune-related genes, and the method is successfully applied to TCGA pancancer data.
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Affiliation(s)
- Yining Xu
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Xinran Cui
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Liyuan Zhang
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Tianyi Zhao
- School of medicine and Health, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Yadong Wang
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
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29
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Hu S, Luo Y, Zhang Z, Xiong H, Yan W, Jiang M, Zhao B. Protein function annotation based on heterogeneous biological networks. BMC Bioinformatics 2022; 23:493. [DOI: 10.1186/s12859-022-05057-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
Abstract
Background
Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data.
Results
In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively.
Conclusions
We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.
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30
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Zhao W, Gu X, Chen S, Wu J, Zhou Z. MODIG: Integrating Multi-Omics and Multi-Dimensional Gene Network for Cancer Driver Gene Identification based on Graph Attention Network Model. Bioinformatics 2022; 38:4901-4907. [PMID: 36094338 DOI: 10.1093/bioinformatics/btac622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 09/07/2022] [Accepted: 09/10/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes. RESULTS Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression, and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein-protein interactions (PPI), gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns, and Gene Ontology (GO). Then, we constructed a multi-dimensional gene network consisting of approximately 20,000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves (AUPR) and area under the receiver operating characteristic curves (AUROC). AVAILABILITY The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wenyi Zhao
- Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.,Collaborative Innovation Center of Artificial Intelligence by MOE and Zhejiang Provincial Government, College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.,Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, 310018, China
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Shuqing Chen
- Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou, 310058, China.,Collaborative Innovation Center of Artificial Intelligence by MOE and Zhejiang Provincial Government, College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.,Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, 310018, China
| | - Zhan Zhou
- Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.,Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, 310018, China.,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China
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31
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Huang H, Zhou G, Liu X, Deng L, Wu C, Zhang D, Liu H. Contrastive learning-based computational histopathology predict differential expression of cancer driver genes. Brief Bioinform 2022; 23:6651307. [PMID: 35901472 DOI: 10.1093/bib/bbac294] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/16/2022] [Accepted: 06/29/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect genetic variations and tumor environment, but few studies focus on differential gene expression in tumor cells. RESULTS In this paper, we propose a self-supervised contrastive learning framework, HistCode, to infer differential gene expression from whole slide images (WSIs). We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological features in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes. Our experiments showed that our method outperformed other state-of-the-art models in tumor diagnosis tasks, and also effectively predicted differential gene expression. Interestingly, we found the genes with higher fold change can be more precisely predicted. To intuitively illustrate the ability to extract informative features from pathological images, we spatially visualized the WSIs colored by the attention scores of image tiles. We found that the tumor and necrosis areas were highly consistent with the annotations of experienced pathologists. Moreover, the spatial heatmap generated by lymphocyte-specific gene expression patterns was also consistent with the manually labeled WSIs.
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Affiliation(s)
- Haojie Huang
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Gongming Zhou
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Xuejun Liu
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Chen Wu
- The third affiliated hospital of Soochow University, 213100, Changzhou, China
| | - Dachuan Zhang
- The third affiliated hospital of Soochow University, 213100, Changzhou, China
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
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32
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Feng Q, Huang Y, Long Y, Gao L, Gao X. A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification. Front Aging Neurosci 2022; 14:925468. [PMID: 35923552 PMCID: PMC9339621 DOI: 10.3389/fnagi.2022.925468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%.
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Affiliation(s)
- Quan Feng
- State Key Laboratory of Public Big Data, GuiZhou University, Guizhou, China
| | - Yongjie Huang
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
| | - Yun Long
- Nanjing Huayin Medical Laboratory Co., Ltd., Nanjing, China
| | - Le Gao
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
- *Correspondence: Le Gao
| | - Xin Gao
- Department of PET/MR, Universal Medical Imaging Diagnostic Center, Shanghai, China
- Xin Gao
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33
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Discovering driver nodes in chronic kidney disease-related networks using Trader as a newly developed algorithm. Comput Biol Med 2022; 148:105892. [PMID: 35932730 DOI: 10.1016/j.compbiomed.2022.105892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/04/2022] [Accepted: 07/16/2022] [Indexed: 11/18/2022]
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34
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Hu S, Zhang Z, Xiong H, Jiang M, Luo Y, Yan W, Zhao B. A tensor-based bi-random walks model for protein function prediction. BMC Bioinformatics 2022; 23:199. [PMID: 35637427 PMCID: PMC9150346 DOI: 10.1186/s12859-022-04747-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/24/2022] [Indexed: 11/26/2022] Open
Abstract
Background The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein–protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. Results In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. Conclusions The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04747-2.
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Affiliation(s)
- Sai Hu
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
| | - Zhihong Zhang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Hunan Provincial Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, Hunan, China
| | - Huijun Xiong
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
| | - Meiping Jiang
- Department of Ultrasound, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.,NHC Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital), Changsha, 410100, Hunan, China
| | - Yingchun Luo
- Department of Ultrasound, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.,NHC Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital), Changsha, 410100, Hunan, China
| | - Wei Yan
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
| | - Bihai Zhao
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China. .,Hunan Provincial Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, Hunan, China.
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35
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Lou Z, Cheng Z, Li H, Teng Z, Liu Y, Tian Z. Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information. Brief Bioinform 2022; 23:6582005. [PMID: 35524503 DOI: 10.1093/bib/bbac159] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/29/2022] [Accepted: 04/10/2022] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION In recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA-disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA-disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging. RESULTS To address such a challenge, we propose a novel method called mixed neighborhood information for miRNA-disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA-disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA. AVAILABILITY AND IMPLEMENTATION https://github.com/chengxu123/MINIMDA and http://120.79.173.96/.
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Affiliation(s)
- Zhengzheng Lou
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Zhaoxu Cheng
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Hui Li
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Yang Liu
- Departments of Cerebrovascular Diseases, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
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36
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Andrades R, Recamonde-Mendoza M. Machine learning methods for prediction of cancer driver genes: a survey paper. Brief Bioinform 2022; 23:6551145. [PMID: 35323900 DOI: 10.1093/bib/bbac062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
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Affiliation(s)
- Renan Andrades
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
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37
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Jia G, Jiang X, Li Z, Ding X, Lei L, Xu S, Gao N. Decoding the Mechanism of Shen Qi Sha Bai Decoction in Treating Acute Myeloid Leukemia Based on Network Pharmacology and Molecular Docking. Front Cell Dev Biol 2022; 9:796757. [PMID: 34988084 PMCID: PMC8720969 DOI: 10.3389/fcell.2021.796757] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 11/17/2021] [Indexed: 12/25/2022] Open
Abstract
Traditional Chinese Medicine (TCM) has been shown to be efficacious in treating leukemia for thousands of years. It has been shown that Shen Qi Sha Bai Decoction (SQSBD) has been extensively used in the treatment of acute myeloid leukemia (AML). However, the mechanism of SQSBD in treating AML remains unclear. In this study, we employed network pharmacology to analyze the potential active components and elucidate molecular mechanism of SQSBD in treating AML. A total of 268 active components were identified from SQSBD, among which 9 key components (Quercetin, luteolin, kaempferol, licochalcone A, formononetin, wogonin, β-sitosterol, oroxylin A, naringenin, and baicalein) were hit by the 6 hub targets (CDK1, MAPK1, JUN, PCNA, HSB1, STAT3) associated with leukemia. Molecular docking showed that two core active components, quercetin and licochalcone A, exhibited the highest component-like properties (DL), and could bind well to CDK1 and MAPK1 protein. The experimental validation of these two components showed that quercetin inhibited cell growth through CDK1 dephosphorylation-mediated cell cycle arrest at G2/M phase in human AML U937 and HL60 cells, and licochalcone A induced cell differentiation in these leukemia cells via activation of MAPK1 and upregulation of CD11b. All these results indicate that SQSBD is effective in the treatment of AML, and quercetin and licochalcone A are the major candidate compounds for AML treatment.
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Affiliation(s)
- Guanfei Jia
- College of Pharmacy, Army Medical University, Chongqing, China
| | - Xiuxing Jiang
- College of Pharmacy, Army Medical University, Chongqing, China
| | - Zhiqiang Li
- College of Pharmacy, Army Medical University, Chongqing, China
| | - Xin Ding
- College of Pharmacy, Army Medical University, Chongqing, China
| | - Ling Lei
- College of Pharmacy, Army Medical University, Chongqing, China
| | - Shuangnian Xu
- Department of Hematology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Ning Gao
- College of Pharmacy, Army Medical University, Chongqing, China.,Key Laboratory of Basic Pharmacology of Ministry of Education, Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, China
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38
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Dai W, Yue W, Peng W, Fu X, Liu L, Liu L. Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network. Genes (Basel) 2021; 13:genes13010065. [PMID: 35052405 PMCID: PMC8774659 DOI: 10.3390/genes13010065] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 11/16/2022] Open
Abstract
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype.
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Affiliation(s)
- Wei Dai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; (W.D.); (W.Y.); (X.F.); (L.L.); (L.L.)
| | - Wenhao Yue
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; (W.D.); (W.Y.); (X.F.); (L.L.); (L.L.)
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; (W.D.); (W.Y.); (X.F.); (L.L.); (L.L.)
- Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
- Correspondence: ; Tel.: +86-13700600056
| | - Xiaodong Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; (W.D.); (W.Y.); (X.F.); (L.L.); (L.L.)
- Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
| | - Li Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; (W.D.); (W.Y.); (X.F.); (L.L.); (L.L.)
- Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
| | - Lijun Liu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China; (W.D.); (W.Y.); (X.F.); (L.L.); (L.L.)
- Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
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