IEEE transactions on computational biology and bioinformatics最新文献_第6页

View-Specific Optimal Rank Based Joint Subspace Clustering for Multi-Omics Cancer Subtyping. 基于视图特定最优秩的联合子空间聚类多组癌亚型。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3729236

Pradipta Maji, Debanjan Chakraborty

{"title":"View-Specific Optimal Rank Based Joint Subspace Clustering for Multi-Omics Cancer Subtyping.","authors":"Pradipta Maji, Debanjan Chakraborty","doi":"10.1109/TCBBIO.2026.3729236","DOIUrl":"10.1109/TCBBIO.2026.3729236","url":null,"abstract":"Data from multiple omics modalities such as genomic, proteomic and transcriptomic are often used simultaneously to leverage consensus and complementary information across the modalities. It facilitates better diagnosis and prognosis of the diseases. However, different modalities or views may have a high degree of heterogeneity in the dimensionality, scale and variance, and are often perturbed with varying amount of noise. The existing methods of clustering multi-omics data typically form a joint subspace from a common low-rank representation of the views, and perform subspace clustering. These approaches, however, fail to capture the heterogeneity in rank of individual views. In this regard, a novel approach is proposed to perform clustering on multi-view data, considering view-specific optimal rank for efficient low-rank representation. A theoretical bound is established on the rank of the shifted Laplacian of each view, in terms of the number of components of the similarity graph. An entropy based regularization is introduced to learn the weight, which depends on the prior relevance of each view. A new quantitative index is proposed to compute the relevance of each view. It not only considers the clustering ability of the given view, but also takes into account the amount of noise present in the view, which is expressed in terms of rank of the view. Rigorous experimentation on multi-omics data sets obtained from The Cancer Genomic Atlas (TCGA) shows that the proposed method performs significantly better than the state-of-the-art methods.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2310-2323"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148870091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAGNN-DTA: Cross-Modal Mutual Attention Graph Neural Networks for Drug-Target Affinity Prediction. 跨模态相互注意图神经网络用于药物-靶点亲和力预测。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3732146

Yun Zhou, Yibo Zhang, Dong Liu, Wei Wang

{"title":"MAGNN-DTA: Cross-Modal Mutual Attention Graph Neural Networks for Drug-Target Affinity Prediction.","authors":"Yun Zhou, Yibo Zhang, Dong Liu, Wei Wang","doi":"10.1109/TCBBIO.2026.3732146","DOIUrl":"10.1109/TCBBIO.2026.3732146","url":null,"abstract":"Drug-Target Affinity (DTA) prediction is a crucial step in developing new pharmaceuticals. However, existing approaches remain limited in capturing cross-modal interactions, often failing to fully characterize complementary information of drugs and proteins at the sequence, structural, and topological levels. To address these limitations, we propose MAGNN-DTA, a cross-modal mutual attention graph neural network framework for DTA prediction. First, MAGNN-DTA adopts a dual-path feature extraction module to extract sequence features and graph features of drug and protein. For drug representation, a Conformer block is used to capture long-range dependencies and local functional patterns, while a Graph Isomorphism Network (GIN) and multi-head attention are used to extract local and global features from molecular graphs. The drug features are further refined via a Kolmogorov-Arnold Network (KAN) to improve representational capacity and interpretability. For protein representation, sequence and structural information are integrated through a multi-scale deep convolutional network and a gated graph attention network (Gated-GAT) derived from residue distance maps. Subsequently, sequence feature and graph feature are concatenated by a gated pooling mechanism to form the representations of the drug and the protein. Finally, the model employs a cross-modal mutual attention module to capture bidirectional interactions between drug and protein modalities, enabling precise identification of key functional groups in drugs and critical binding residues in proteins. Extensive experiments on the Davis and KIBA benchmark datasets demonstrate that MAGNN-DTA achieves highly competitive performance compared with recent advanced methods across standard evaluation metrics.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2360-2371"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148915060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of GO Terms Based on Partitioning PPI Networks Into Highly Connected Components. 基于PPI网络高连通组件划分的GO项预测。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3706474

Milana Grbic, Branislava Gemovic, Savka Vracevic, Radoslav Davidovic, Dragan Matic, Aleksandar Kartelj

引用次数: 0

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-Task Learning Method. 通过一种新的多任务学习方法探索脑成像遗传学的复杂遗传机制。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3709159

Yiming Wang, Cui-Na Jiao, Ying-Lian Gao, Xin-Chun Cui, Chun-Hou Zheng, Jin-Xing Liu

{"title":"Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-Task Learning Method.","authors":"Yiming Wang, Cui-Na Jiao, Ying-Lian Gao, Xin-Chun Cui, Chun-Hou Zheng, Jin-Xing Liu","doi":"10.1109/TCBBIO.2026.3709159","DOIUrl":"10.1109/TCBBIO.2026.3709159","url":null,"abstract":"Brain imaging genetics generally combines genotype data with brain structure and functional measures to investigate the genetic basis of neurological disorders. Multimodal brain imaging data carry different but complementary information, which can clearly measure the same brain. Multi-task sparse canonical correlation analysis (MTSCCA) is often used to analyze the bi-multivariate correlation between genotype data and multimodal imaging features; however, it lacks the capacity to identify class-specific biomarkers and to deeply capture complex genetic mechanisms. In this paper, a linear discrimination and decomposition method based on MTSCCA (LDMTSCCA) is proposed to solve above problems. Specifically, LDMTSCCA first extracts disease-related genetic information using sparse linear discriminant analysis, and then employs parameter decomposition to learn multi-level expression patterns of genetic loci. To better explore intricate genetic mechanisms, LDMTSCCA jointly incorporates disease states, parameter decomposition, and network connectivity constraints into the association analysis between multilocus genetic information and multimodal neuroimaging phenotypes. The proposed method is compared with the traditional single task, multi-task, multi-view, and deep learning-based canonical correlation analysis methods in Alzheimer's Disease Neuroimaging Initiative, which shows that LDMTSCC not only achieves the highest canonical correlation coefficients but also identifies multi-level biomarkers.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"1893-1903"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TF-MOEA$^{+}$: A Multi-Objective Evolutionary Framework With Self-Adaptive Topological-Functional Mutation for Protein Complex Detection. TF-MOEA$^{+}$：基于自适应拓扑功能突变的蛋白质复合物检测多目标进化框架。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3710847

Mustafa N Abbas, David Broneske, Gunter Saake

{"title":"TF-MOEA$^{+}$: A Multi-Objective Evolutionary Framework With Self-Adaptive Topological-Functional Mutation for Protein Complex Detection.","authors":"Mustafa N Abbas, David Broneske, Gunter Saake","doi":"10.1109/TCBBIO.2026.3710847","DOIUrl":"10.1109/TCBBIO.2026.3710847","url":null,"abstract":"Identifying protein complexes from protein-protein interaction (PPI) networks is a fundamental task in systems biology, offering important insights into cellular organization and molecular function. However, reliable complex detection remains challenging due to the sparsity and noise of PPI data, as well as the need to balance structural compactness, inter-complex separability, and biological coherence. To address these challenges, this study formulates protein complex detection as a tri-objective optimization problem that jointly integrates topological and biological criteria. We propose TF-MOEA$^{+}$, a multi-objective evolutionary framework built on NSGA-II and equipped with two biologically informed search operators. The first is an objective-guided uniform crossover (OG-UX), which biases recombination toward parents with better multi-objective quality while preserving adjacency-feasible inheritance. The second is a self-adaptive topological-functional synergy mutation (TF-SM), which adjusts mutation behavior according to the degree of structural and functional integration of each protein and reassigns weakly integrated proteins using combined topological and GO-based evidence. Comprehensive experiments on four benchmark yeast PPI networks, Yeast-D1, Yeast-D2, Collins-CYC2008, and Collins-MIPS, show that TF-MOEA$^{+}$ consistently outperforms all baseline methods considered in this study in both detection accuracy and functional relevance. Additional ablation, statistical significance, sensitivity, and runtime analyses further demonstrate the robustness and efficiency of the proposed framework.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"1981-1994"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

READ: A Retrieval-Alignment Diffusion Framework for Structure-Based Drug Design. 基于结构的药物设计的检索-对准扩散框架。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3721445

Dong Xu, Zhangfan Yang, Junchuang Cai, Sisi Yuan, Zexuan Zhu, Jianqiang Li, Junkai Ji

{"title":"READ: A Retrieval-Alignment Diffusion Framework for Structure-Based Drug Design.","authors":"Dong Xu, Zhangfan Yang, Junchuang Cai, Sisi Yuan, Zexuan Zhu, Jianqiang Li, Junkai Ji","doi":"10.1109/TCBBIO.2026.3721445","DOIUrl":"10.1109/TCBBIO.2026.3721445","url":null,"abstract":"Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution. However, most existing approaches frame molecular generation as an isolated optimization or a one-to-one matching task, overlooking the shared binding patterns and intrinsic similarities among protein-ligand complexes. This fragmented perspective constrains their ability to capture the fundamental principles governing molecular recognition and binding specificity. Moreover, the limited availability of high-quality experimental data further hampers model generalization and real-world applicability. To address these challenges, we present READ, a retrieval-alignment molecular generation framework that conditions the generative process on small molecules targeting homologous proteins. Retrieved ligands are aligned with a diffusion model across multiple representational spaces and integrated as conditional guidance throughout successive stages of generation. Under a standardized docking-based evaluation protocol, READ achieves consistently strong performance against state-of-the-art SBDD methods. More importantly, it introduces a retrieval-alignment paradigm for structure-based molecular generation, offering a practical framework for early-stage computational hit generation while leaving prospective experimental validation as future work.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2140-2154"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148687579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Graph Transformer Framework for Predicting Drug-Disease Associations With Structural Awareness. 基于结构意识预测药物-疾病关联的新型图转换框架。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3721658

Bo-Wei Zhao, Hui-Feng Zhao, Yu-An Huang, Peng-Wei Hu, Zhu-Hong You, Lun Hu

{"title":"A Novel Graph Transformer Framework for Predicting Drug-Disease Associations With Structural Awareness.","authors":"Bo-Wei Zhao, Hui-Feng Zhao, Yu-An Huang, Peng-Wei Hu, Zhu-Hong You, Lun Hu","doi":"10.1109/TCBBIO.2026.3721658","DOIUrl":"10.1109/TCBBIO.2026.3721658","url":null,"abstract":"Accurately predicting drug-disease associations (DDAs) is essential for accelerating the discovery of novel therapeutics. Graph representation learning-based computational models have become increasingly popular for this task due to their efficiency and cost-effectiveness. However, existing approaches often suffer from structural inductive biases and a limited ability to capture the rich heterogeneous context of biomedical molecules, which constrains their capacity to learn expressive drug and disease representations. To address this issue, we propose SGTL-DDA, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs). SGTL-DDA integrates a meta-path-guided sampling strategy with a multi-level attention mechanism, enabling the model to jointly learn from both structural dependencies and attribute semantics in an end-to-end manner. Extensive experiments on two benchmark datasets demonstrate that SGTL-DDA consistently outperforms state-of-the-art methods in terms of Accuracy, F1-score, and AUC under a ten-fold cross-validation scheme. Furthermore, case studies on Alzheimer's disease and breast cancer confirm the predictive capability of SGTL-DDA, as it successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2114-2125"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148687477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

scCMIA: Mutual Information-Guided Decoupled Learning for Robust Single-Cell Cross-Modal Integration. 鲁棒单细胞跨模态集成的互信息引导解耦学习。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3729857

Xuanwei Lin, Pengzhen Hu, Hebing Chen, Ximeng Liu, Xiaochen Bo, Hao Li

{"title":"scCMIA: Mutual Information-Guided Decoupled Learning for Robust Single-Cell Cross-Modal Integration.","authors":"Xuanwei Lin, Pengzhen Hu, Hebing Chen, Ximeng Liu, Xiaochen Bo, Hao Li","doi":"10.1109/TCBBIO.2026.3729857","DOIUrl":"10.1109/TCBBIO.2026.3729857","url":null,"abstract":"Recent advances in single-cell multimodal omics sequencing enable the joint profiling of multiple molecular layers within individual cells. Despite this progress, computational integration remains challenging because cross-modal alignment must be achieved without discarding modality-specific information. This paper introduces scCMIA, a mutual-information-guided framework for robust single-cell cross-modal integration. scCMIA decomposes the representation of each modality into a semantic latent variable for shared cellular states and a modality-specific latent variable for non-shared information required for reconstruction. The framework combines contrastive cross-modal alignment, mutual-information-guided decoupling, and a unified CrossVQ codebook to support both accurate reconstruction and interpretable discrete representation learning. Benchmarking across paired single-cell multi-omics datasets demonstrates that scCMIA achieves strong alignment and reconstruction performance, improves downstream label transfer and cell-type classification, and enables code-level analysis of cross-modal coupling patterns across cell types. These results show that scCMIA provides an effective and interpretable framework for single-cell cross-modal integration.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148877083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EGA-DTA: An Energetic-Geometric Augmented Graph Neural Network With Target-Conditional Gating for DTA Prediction. EGA-DTA：基于目标条件门控的能量几何增广图神经网络。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3724188

Zhilin Zhu, Yifan Wu, Junkai Wang, Gang Luo, Zhangli Lu, Min Li

{"title":"EGA-DTA: An Energetic-Geometric Augmented Graph Neural Network With Target-Conditional Gating for DTA Prediction.","authors":"Zhilin Zhu, Yifan Wu, Junkai Wang, Gang Luo, Zhangli Lu, Min Li","doi":"10.1109/TCBBIO.2026.3724188","DOIUrl":"10.1109/TCBBIO.2026.3724188","url":null,"abstract":"Drug-target affinity (DTA) prediction plays an important role in virtual screening. However, many graph-based DTA models represent molecular bonds solely as discrete bond types and combine drug and protein features through simple concatenation. We propose EGA-DTA, a graph neural network augmented with energetic and geometric edge features and target-conditional gating. EGA-DTA characterizes ligand bonds using bond dissociation energy (BDE) and conformer-derived bond length (BL), integrates graph-based and fingerprint-based drug representations, and employs a protein-derived gating mechanism to modulate the drug representations before fusion. On the Davis, KIBA, and Metz benchmarks, EGA-DTA achieves competitive mean squared error (MSE) and concordance index (CI), together with a favorable $r_{m}^{2}$ value on Davis. On the same fixed KIBA cold-start splits, EGA-DTA demonstrates competitive performance, yielding the lowest MSE point estimate among the reproduced baselines in the cold-target, cold-drug, and cold-pair settings. Controlled standard-split ablations further suggest that both the physicochemical edge encoding and the target-conditional gating mechanism may contribute to the observed performance.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"2222-2233"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148764552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling and Tracking of Heterogeneous Cell Populations via Open Multi-Agent Systems. 基于开放多智能体系统的异质细胞群建模与跟踪。

IEEE transactions on computational biology and bioinformatics Pub Date : 2026-09-01 DOI: 10.1109/TCBBIO.2026.3705938

Andrea Tramaloni, Andrea Testa, Sofia Avnet, Stefania Massari, Gemma Di Pompo, Nicola Baldini, Giuseppe Notarstefano

{"title":"Modeling and Tracking of Heterogeneous Cell Populations via Open Multi-Agent Systems.","authors":"Andrea Tramaloni, Andrea Testa, Sofia Avnet, Stefania Massari, Gemma Di Pompo, Nicola Baldini, Giuseppe Notarstefano","doi":"10.1109/TCBBIO.2026.3705938","DOIUrl":"10.1109/TCBBIO.2026.3705938","url":null,"abstract":"Understanding cellular dynamics represents a critical challenge in biomedical research. Optical microscopy remains a key technique for observing live-cell behaviors in vitro. This paper introduces an enhanced cell-tracking algorithm designed to address dynamic changes in cell populations, including mitosis, migration, and cell-cell interactions, even within complex co-culture models. The proposed method involves three main steps: 1) modeling the movements and interactions of different cell types in co-culture experiments via tailored open multi-agent systems; 2) identifying parameters via real data for a multi-agent, multi-culture framework; 3) embedding the model within an Extended Kalman Filter, to predict the dynamics of heterogeneous cell populations across video frames. To validate the approach, we used a novel dataset involving the interplay between tumor and normal cells, namely osteosarcoma and mesenchymal stromal cells, respectively. This dataset offers a challenging and clinically relevant framework to track cell proliferation and study how cancer cells evolve and interact with stromal cells within their surroundings. Performance metrics demonstrated the effectiveness of the algorithm over state-of-the-art methodologies, highlighting its ability to track heterogeneous cell types, capture their interactions, and generate the estimated cell lineage tree.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":"1879-1892"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148304541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0