Briefings in bioinformatics最新文献_第8页

MVSGDR: multi-view stacked graph convolutional network for drug repositioning. MVSGDR：用于药物重定位的多视图堆叠图卷积网络。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf396

Guosheng Gu, Haowei Wu, Haojie Han, Zhiyi Lin, Yuping Sun, Guobo Xie, Qing Su, Zhenguo Liu

{"title":"MVSGDR: multi-view stacked graph convolutional network for drug repositioning.","authors":"Guosheng Gu, Haowei Wu, Haojie Han, Zhiyi Lin, Yuping Sun, Guobo Xie, Qing Su, Zhenguo Liu","doi":"10.1093/bib/bbaf396","DOIUrl":"10.1093/bib/bbaf396","url":null,"abstract":"Drug repositioning (DR) presents a cost-effective strategy for drug development by identifying novel therapeutic applications for existing drugs. Current computational approaches remain constrained by their inability to synergize localized substructure patterns with global network semantics, leading to overreliance on data augmentation to mitigate latent drug-disease association (DDA) information gaps. To address these limitations, we present multi-view stacked graph convolutional network (MVSGDR), a novel DR framework featuring three technical innovations: (i) multi-view stacked module that enables depth-wise feature enhancement through hierarchical aggregation of multi-hop neighborhood interactions across distinct graph convolutional layers; (ii) bi-level subgraph transformer module that decomposes DDAs into METIS (a graph partitioning tool) informative subgraphs for breadth-wise analysis of external and internal subgraph drug-disease relationships; and (iii) negative sampling balancing strategy that mitigates sample imbalance through negative sample synthesis. Extensive 10-fold cross-validation experiments across four benchmark datasets confirm MVSGDR's superior performance, demonstrating its statistically significant improvements over existing methods. Moreover, case studies further validate MVSGDR's potential utility through identification of previously unreported DDAs with supporting literature evidence.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12403086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144943443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

scBCN: deep learning-based batch correction network for integration of heterogeneous single-cell data. scBCN：基于深度学习的异构单细胞数据集成批校正网络。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf503

Lei Wan, Yang Zhou, Xingzhi Wang, Jing Qi, Shuilin Jin

引用次数: 0

DeCoST: unveiling cell type heterogeneity in spatial transcriptomics based on inter-domain alignment and Gaussian kernel conditional autoregressive. 揭示基于域间比对和高斯核条件自回归的空间转录组学中的细胞类型异质性。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf490

Xinyang Guo, Zilin Li, Zhaoyang Huang, Juan Li, Chenguang Zhao, Liang Yu

{"title":"DeCoST: unveiling cell type heterogeneity in spatial transcriptomics based on inter-domain alignment and Gaussian kernel conditional autoregressive.","authors":"Xinyang Guo, Zilin Li, Zhaoyang Huang, Juan Li, Chenguang Zhao, Liang Yu","doi":"10.1093/bib/bbaf490","DOIUrl":"10.1093/bib/bbaf490","url":null,"abstract":"Spatial transcriptomics (STs) has emerged as a transformative approach to elucidate cellular heterogeneity and spatial organization within complex tissue microenvironments. However, the analysis of ST data is challenged by limited spatial resolution, resulting in mixed expression profiles at each spatial location. Moreover, the precious spatial information is rarely exploited, and noise issues in spatial transcriptomes (STs) are often overlooked by computational deconvolution methods. In this study, a novel computational framework for STs deconvolution (DeCoST), called DeCoST, is presented. DeCoST capitalizes on the valuable spatial context information by integrating a Gaussian kernel-based conditional autoregressive model. Additionally, the method employs domain adaptation techniques to address platform effects between single-cell and ST data, enabling robust cell type identification. Evaluations on simulated datasets under diverse spatial configurations, as well as real-world case studies on human pancreatic ductal adenocarcinoma, mouse olfactory bulb, and mouse brain samples, demonstrate the superior performance of DeCoST compared to existing deconvolution approaches. The method's ability to accurately map region-specific cell types and uncover spatial interactions advances our understanding of complex tissue organization and function, with broad applications in disease research and developmental biology.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145130042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Growing and linking optimizers: synthesis-driven molecule design. 生长和连接优化器：合成驱动的分子设计。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf482

Clarisse Descamps, Vincent Bouttier, Juan Sanz García, Maoussi Lhuillier-Akakpo, Quentin Perron, Hamza Tajmouati

{"title":"Growing and linking optimizers: synthesis-driven molecule design.","authors":"Clarisse Descamps, Vincent Bouttier, Juan Sanz García, Maoussi Lhuillier-Akakpo, Quentin Perron, Hamza Tajmouati","doi":"10.1093/bib/bbaf482","DOIUrl":"10.1093/bib/bbaf482","url":null,"abstract":"In the present work, two reaction-based generative models for molecular design are presented: growing optimizer and linking optimizer. These models are designed to emulate real-life chemical synthesis by sequentially selecting building blocks and simulating the reactions between them to form new compounds. By focusing on the feasibility of the generated molecules, growing optimizer and linking optimizer offer several advantages, including the ability to restrict chemistry to specific building blocks, reaction types, and synthesis pathways, a crucial requirement in drug design. Unlike text-based models, which construct molecules by iteratively forming a textual representation of the molecular structure, and graph-based models, which assemble molecules atom by atom or fragment by fragment, our approach incorporates a more comprehensive understanding of chemical knowledge, making it relevant for drug discovery projects. Comparative analysis with REINVENT 4, a state-of-the-art molecular generative model, shows that growing optimizer and linking optimizer are more likely to produce synthetically accessible molecules while reaching molecules of interest with the desired properties.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145130060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-protein embedding-based graph model with dynamic attention for interaction prediction. 基于双蛋白嵌入的动态关注交互预测图模型。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf517

Shunpeng Pang, Mingjian Jiang, Shugang Zhang, Shuang Wang, Zhen Li, Jing Sun, Yuanyuan Zhang, Li Guo

{"title":"Dual-protein embedding-based graph model with dynamic attention for interaction prediction.","authors":"Shunpeng Pang, Mingjian Jiang, Shugang Zhang, Shuang Wang, Zhen Li, Jing Sun, Yuanyuan Zhang, Li Guo","doi":"10.1093/bib/bbaf517","DOIUrl":"10.1093/bib/bbaf517","url":null,"abstract":"Protein-protein interactions (PPIs) are fundamental to biological processes, yet experimental determination of PPIs remains costly and labor-intensive. While computational methods have emerged as promising alternatives, sequence-based approaches face critical challenges: (1) effectively capturing long-range dependencies and critical biochemical patterns in variable-length sequences, and (2) balancing computational efficiency with sensitivity to subtle residue-level interactions. Here, we present Dual Protein Embedding-based Graph Model (DPEG), which leverages dynamic graph attention networks to enable robust sequence-driven PPI prediction. Unlike structure-dependent methods, DPEG operates solely on sequence data, bypassing the need for structural or domain annotations. Specifically, we employ ESM-2 to transform sequences into residue-level graphs, preserving evolutionary and physicochemical context. To address variable sequence lengths, we design a module that can represent protein sequences of arbitrary lengths as graph networks at the amino acid level. Further, a gated attention mechanism is introduced to adaptively refining residue representations. Finally, a dynamic attention mechanism prioritizes functionally critical motifs within the graph. Evaluated on four diverse PPI datasets spanning different species and interaction types, DPEG achieves state-of-the-art performance and demonstrates strong cross-dataset generalizability. By integrating deep sequence semantics with graph-based interaction modeling, DPEG advances sequence-only PPI prediction, offering a scalable and biologically plausible framework for proteome-wide studies.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational immunology in venom research: a systematic review of epitope prediction and validation approaches. 计算免疫学在毒液研究：表位预测和验证方法的系统回顾。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf519

Razana Zegrari, Abderrahim Ait Ouchaoui, Zainab Gaouzi, Hanane Abbou, Rihab Festali, Rachid Eljaoudi, Saber Boutayeb, Lahcen Belyamani, Ilhame Bourais

{"title":"Computational immunology in venom research: a systematic review of epitope prediction and validation approaches.","authors":"Razana Zegrari, Abderrahim Ait Ouchaoui, Zainab Gaouzi, Hanane Abbou, Rihab Festali, Rachid Eljaoudi, Saber Boutayeb, Lahcen Belyamani, Ilhame Bourais","doi":"10.1093/bib/bbaf519","DOIUrl":"10.1093/bib/bbaf519","url":null,"abstract":"Venom-based therapies are hindered by traditional discovery methods that are costly and inconsistent. Immunoinformatics offers a faster route to identify immunogenic epitopes, yet its application to venom proteins remains limited. We conducted a systematic review under PRISMA-2020 guidelines to identify studies predicting venom toxin epitopes computationally and validating them experimentally. Risk of bias was evaluated using a custom 20-question checklist. Following our systematic search, 11 articles met inclusion criteria. Multitool prediction strategies consistently outperformed single-tool approaches, particularly when structural and sequence-based models were combined. Experimental validations confirmed immunogenicity through diverse assays, but reporting inconsistencies, limited negative data, and variable study designs impaired direct comparison. Toxin family and structural data availability emerged as key factors influencing prediction success. In silico epitope prediction, combined with experimental validation, holds strong promise for advancing venom research. Our systematic bias assessment underscores the critical need for standardized frameworks to evaluate dataset selection, algorithm parameters, and validation rigor in computational epitope discovery. Moreover, the field must urgently address data scarcity, standardize validation protocols, and expand venom-specific training datasets to fully realize the promise of immunoinformatics-driven discovery.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes. 在单细胞分辨率下导航3D基因组：技术，计算和机械景观。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf520

Feitong Hong, Kaiyuan Han, Yuduo Hao, Wei Su, Xueqin Xie, Xiaolong Li, Qiuming Chen, Yijie Wei, Xinwei Luo, Sijia Xie, Benjamin Lebeau, Crystal Ling, Hao Lv, Li Liu, Hao Lin, Fuying Dao

{"title":"Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.","authors":"Feitong Hong, Kaiyuan Han, Yuduo Hao, Wei Su, Xueqin Xie, Xiaolong Li, Qiuming Chen, Yijie Wei, Xinwei Luo, Sijia Xie, Benjamin Lebeau, Crystal Ling, Hao Lv, Li Liu, Hao Lin, Fuying Dao","doi":"10.1093/bib/bbaf520","DOIUrl":"https://doi.org/10.1093/bib/bbaf520","url":null,"abstract":"The 3D organization of the genome is critical for gene expression regulation, cellular identity, and disease progression. Traditional methods that analyze bulk genomic data often obscure cell-to-cell heterogeneity, limiting the resolution of intrinsic variability within complex biological systems. To overcome this, single-cell 3D genomics has emerged, revealing chromatin architecture at the individual cell level. Advanced experimental approaches enable genome-wide chromatin contact mapping, while computational frameworks reconstruct dynamic chromatin topologies from high-dimensional data. Building on these breakthroughs, recent advances in single-cell 3D genomics have led to transformative progress in epigenetics, linking 3D genome architecture with gene regulation, cellular identity, and disease phenotypes. This review focuses on the breakthroughs in single-cell 3D genomics, demonstrating how integrated experimental, computational, and mechanistic approaches decode chromatin architecture. These insights have deepened the understanding of genome function at the single-cell level and lay the foundation for future advances in precision medicine and topology-guided therapeutic strategies.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GRAPE: graph-regularized protein language modeling unlocks TCR-epitope binding specificity. GRAPE：图正则化蛋白质语言建模解锁tcr表位结合特异性。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf522

Xiangzheng Fu, Li Peng, Haowen Chen, Mingqiang Rong, Yifan Chen, Dongsheng Cao, Sisi Yuan, Aiping Lu

{"title":"GRAPE: graph-regularized protein language modeling unlocks TCR-epitope binding specificity.","authors":"Xiangzheng Fu, Li Peng, Haowen Chen, Mingqiang Rong, Yifan Chen, Dongsheng Cao, Sisi Yuan, Aiping Lu","doi":"10.1093/bib/bbaf522","DOIUrl":"https://doi.org/10.1093/bib/bbaf522","url":null,"abstract":"T-cell receptor (TCR)-epitope binding prediction is critical for immunotherapies but remains challenged by sparse interaction networks and severe class imbalance in training data. Current graph neural network (GNN) approaches for predicting TCR-epitope binding (TEB) fail to address two key limitations: over-smoothing during message propagation in sparse TCR-epitope graphs and biased predictions toward dominant epitope-TCR pairs. Here, we present GRAPE (Graph-Regularized Attentive Protein Embeddings), a framework unifying spectral graph regularization and imbalance-aware learning. GRAPE first leverages protein language models (ESM-2) to generate evolutionary-informed TCR/epitope embeddings, constructing a topology-aware interaction graph. To mitigate over-smoothing, we introduce spectral graph regularization, explicitly constraining node feature smoothness to preserve discriminative patterns in sparse neighborhoods. Simultaneously, a dynamic edge reweighting module prioritizes unobserved TCR-epitope edges during graph propagation, coupled with a differentiable area under the ROC curve-maximization objective that directly optimizes for imbalance resilience. Extensive benchmarking on public datasets demonstrates that GRAPE significantly outperforms state-of-the-art methods in TEB prediction. This work establishes GRAPE as a robust framework for elucidating TCR-epitope interactions, with broad applications in immunology research and therapeutic design.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

INAB: identify nucleic acid binding domain via cross-modal protein language models and multiscale computation. INAB：通过跨模态蛋白质语言模型和多尺度计算识别核酸结合域。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf509

Jun Zhang, Hao Zeng, Junjie Chen, Zexuan Zhu

引用次数: 0

BioWalk-MDA: a novel approach for large-scale predicting metabolite-drug associations based on multi layered biomedical knowledge graphs. BioWalk-MDA：一种基于多层生物医学知识图的大规模预测代谢物-药物关联的新方法。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf480

Xiaoliang Wu, Meitao Wu, Yetong Yang, Shuo Jiang, Gen Li, Yanghe Fu, Zhuoxin Liu, Yingli Lv, Hongbo Shi

{"title":"BioWalk-MDA: a novel approach for large-scale predicting metabolite-drug associations based on multi layered biomedical knowledge graphs.","authors":"Xiaoliang Wu, Meitao Wu, Yetong Yang, Shuo Jiang, Gen Li, Yanghe Fu, Zhuoxin Liu, Yingli Lv, Hongbo Shi","doi":"10.1093/bib/bbaf480","DOIUrl":"10.1093/bib/bbaf480","url":null,"abstract":"Metabolism is fundamental to sustaining human life, with changes in metabolite levels closely related to the occurrence and progression of diseases. The interaction between metabolites and drugs is intricate, encompassing drugs can modulate metabolite concentrations, as well as the metabolites generated through drug metabolism can influence pharmacological toxicity and drug interactions. Currently, a substantial proportion of metabolite-drug associations remains to be fully elucidated, and the data from individual sources are often characterized by incompleteness and noise. Here, we present BioWalk-MDA, a computational framework for large-scale predicting novel interactions between 8354 metabolites and 11 570 drugs. The framework constructs multilayered biomedical knowledge graphs (Multi-BiomedKGs) by integrating biological information across proteins, microbes, and diseases, and incorporated five types of graphs and seven types of associations. It employed random walk and heterogeneous Skip-gram model to extract feature vectors of metabolite-drug pairs and utilized a fully connected neural network (FCNN) to infer novel metabolite-drug associations. The framework demonstrated exceptional performance with an average accuracy of 0.971, an area under the receiver operating characteristic curve (AUROC) value of 0.995, and an area under the precision-recall curve (AUPRC) value of 0.994 in 5-fold cross-validation, surpassing other similar methods. Case studies on three metabolites detectable in blood and three cardiovascular drugs further demonstrated the reliability and efficiency of BioWalk-MDA, and it is anticipated to serve as a valuable tool for exploring metabolite-drug interactions and aiding in drug development and combination strategies.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12450351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0