Briefings in bioinformatics最新文献

{"title":"scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.","authors":"Yao Dong, Jiaxue Zhang, Jin Shi, Yushan Hu, Xiaowen Cao, Yongfeng Dong, Xuekui Zhang","doi":"10.1093/bib/bbaf244","DOIUrl":"10.1093/bib/bbaf244","url":null,"abstract":"<p><p>Single-cell multi-omics clustering confronts noise and heterogeneity barriers. Current multi-view anchor graph approaches, though successful in noise reduction, inadequately model higher order feature interactions. To address this issue, we propose scAGCI, a cell clustering method based on anchor graphs that integrates both scRNA-seq and scATAC-seq data. Our method captures specific and shared anchor graphs representing the properties of omics data in the process of dynamic anchor unification, and mines high-order shared information to complete the omics representation. Subsequently, clustering results are obtained by integrating the specific and shared omics representation. Benchmarking against 13 state-of-the-art methods confirms scAGCI's superior clustering performance and computational efficiency in cell-type identification and subtype resolution. The method preserves biologically meaningful omics patterns, as evidenced by marker gene enrichment and functional analyses, establishing it as a robust tool for elucidating cellular heterogeneity in single-cell multi-omics data.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12232420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574848","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}

{"title":"Kinase-inhibitor binding affinity prediction with pretrained graph encoder and language model.","authors":"Xudong Guo, Zixu Ran, Fuyi Li","doi":"10.1093/bib/bbaf338","DOIUrl":"10.1093/bib/bbaf338","url":null,"abstract":"<p><p>Accurately predicting inhibitor-kinase binding affinity is crucial in drug discovery and medical applications, especially in the treatment of diseases such as cancer. Existing methods for predicting inhibitor-kinase affinity still face challenges, including insufficient data expression, limited feature extraction, and low performance. Despite the progress made through artificial intelligence methods, particularly deep learning technology, many current methods fail to capture the intricate interactions between kinases and inhibitors. Therefore, it is necessary to develop more advanced methods to solve the existing problems in inhibitor-kinase binding prediction. This study proposed Kinhibit, a novel framework for inhibitor-kinase binding affinity prediction. Kinhibit integrates self-supervised graph contrastive learning with multiview molecular graph representation and structure-informed protein language model (ESM-S) to extract features effectively. Kinhibit also employed a feature fusion approach to optimize the fusion of inhibitor and kinase features. Experimental results demonstrate the superiority of this method, achieving an accuracy of 92.6% in inhibitor prediction tasks of three mitogen-activated protein kinase (MAPK) signalling pathway kinases: Raf protein kinase (RAF), Mitogen-activated protein kinase kinase (MEK), and Extracellular Signal-Regulated Kinase (ERK). Furthermore, the framework achieves an impressive accuracy of 92.9% on the MAPK-All dataset. This study provides promising and effective tools for drug screening and biological sciences.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641766","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}

{"title":"AI-guided discovery and optimization of antimicrobial peptides through species-aware language model.","authors":"Daehun Bae, Minsang Kim, Jiwon Seo, Hojung Nam","doi":"10.1093/bib/bbaf343","DOIUrl":"https://doi.org/10.1093/bib/bbaf343","url":null,"abstract":"<p><p>The rise of antibiotic-resistant bacteria drives an urgent need for novel antimicrobial agents. Antimicrobial peptides (AMPs) show promise solutions due to their multiple mechanisms of action and reduced propensity for resistance development. This study introduces LLAMP (Large Language model for AMP activity prediction), a target species-aware AI model that leverages pre-trained language models to predict minimum inhibitory concentration values of AMPs. Using LLAMP, we screened approximately 5.5 million peptide sequences, identifying peptides 13 and 16 as the most selective and most potent candidates, respectively. Analysis of attention values allowed us to pinpoint critical amino acid residues (e.g., Trp, Lys, and Phe). Using the critical amino acids, the sequence of the most selective peptide 13 was engineered to increase amphipathicity through targeted modifications, yielding peptide 13-5 with an overall enhancement in antimicrobial activity but a reduction in selectively. Notably, peptides 13-5 and 16 demonstrated antimicrobial potency and selectivity comparable to the clinically investigated AMP pexiganan. Our work demonstrates the potential of AI to expedite the discovery of peptide-based antibiotics to combat antibiotic resistance.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658479","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}

{"title":"A data-intelligence-intensive bioinformatics copilot system for large-scale omics research and scientific insights.","authors":"Yang Liu, Rongbo Shen, Lu Zhou, Qingyu Xiao, Jiao Yuan, Yixue Li","doi":"10.1093/bib/bbaf312","DOIUrl":"10.1093/bib/bbaf312","url":null,"abstract":"<p><p>Advancements in high-throughput sequencing technologies and artificial intelligence (AI) offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of AI technologies require automated big biological data analysis capability and interdisciplinary knowledge-driven scientific insight. Here, we propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system that synergizes AI capabilities with human researchers to facilitate hypothesis-free exploratory research and inspire novel scientific insights in large-scale omics studies. Bio-Copilot forms high-quality intensive intelligence through close collaboration between multiple agents, driven by large language models (LLMs), and human researchers. To augment the capabilities of Bio-Copilot, this study devises an agent group management strategy, an effective human-agent interaction mechanism, a shared interdisciplinary knowledge database, and continuous learning strategies for the agents. We comprehensively compare Bio-Copilot against GPT-4o and several leading AI agents across diverse bioinformatics tasks, using a broad range of evaluation metrics. Bio-Copilot achieves overall state-of-the-art performance across all tasks, while showcasing exceptional task completeness. Furthermore, on application to constructing a large-scale human lung cell atlas, Bio-Copilot not only reproduces the intricate data integration process detailed in a seminal study but also introduces a recursive, multilevel annotation strategy to capture the continuous nature of cellular states and uncovers the characteristics of rare cell types, highlighting its potential to unravel hidden complexities in biological systems. Beyond the technical achievements, this study also underscores the profound implications of integrating AI capabilities with expert knowledge in accelerating impactful biological discoveries and exploring uncharted territories.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607393","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}

{"title":"scRDAN: a robust domain adaptation network for cell type annotation across single-cell RNA sequencing data.","authors":"Yan Sun, Yan Zhao, Junliang Shang, Baojuan Qin, Xiaohan Zhang, Jin-Xing Liu","doi":"10.1093/bib/bbaf344","DOIUrl":"10.1093/bib/bbaf344","url":null,"abstract":"<p><p>Single-cell RNA sequencing technology facilitates the recognition of diverse cell types and subgroups, playing a crucial role in investigating cellular heterogeneity. Cell type annotation, a crucial process in single-cell RNA sequencing analysis, is often influenced by noise and batch effects. To address these challenges, we propose scRDAN, which is a robust domain adaptation network comprising three modules: the denoising domain adaptation module, the fine-grained discrimination module, and the robustness enhancement module. The denoising domain adaptation module mitigates noise interference through feature reconstruction in domains, while leveraging adversarial learning to align data distributions, improving annotation accuracy and robustness against batch effects. The fine-grained discrimination module maintains intra-class compactness and enhances inter-class separability, reducing feature overlap and improving cell type distinction. Finally, the robustness enhancement module introduces noise from various perspectives in both domains, enhancing robustness and generalization. We evaluate scRDAN on simulated, cross-platforms, and cross-species datasets, comparing it with advanced methods. Results demonstrate that scRDAN outperforms existing methods in handling batch effects and cell type annotation.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144648531","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}

{"title":"MetaGeno: a chromosome-wise multi-task genomic framework for ischaemic stroke risk prediction.","authors":"Yue Yang, Kairui Guo, Yonggang Zhang, Zhen Fang, Hua Lin, Mark Grosser, Deon Venter, Weihai Lu, Mengjia Wu, Dennis Cordato, Guangquan Zhang, Jie Lu","doi":"10.1093/bib/bbaf348","DOIUrl":"https://doi.org/10.1093/bib/bbaf348","url":null,"abstract":"<p><p>Current genome-wide association studies provide valuable insights into the genetic basis of ischaemic stroke (IS) risk. However, polygenic risk scores, the most widely used method for genetic risk prediction, have notable limitations due to their linear nature and inability to capture complex, nonlinear interactions among genetic variants. While deep neural networks offer advantages in modeling these complex relationships, the multifactorial nature of IS and the influence of modifiable risk factors present additional challenges for genetic risk prediction. To address these challenges, we propose a Chromosome-wise Multi-task Genomic (MetaGeno) framework that utilizes genetic data from IS and five related diseases. The framework includes a chromosome-based embedding layer to model local and global interactions among adjacent variants, enabling a biologically informed approach. Incorporating multi-disease learning further enhances predictive accuracy by leveraging shared genetic information. Among various sequential models tested, the Transformer demonstrated superior performance, and outperformed other machine learning models and PRS baselines, achieving an AUROC of 0.809 on the UK Biobank dataset. Risk stratification identified a two-fold increased stroke risk (HR, 2.14; 95% CI: 1.81-2.46) in the top 1% risk group, with a nearly five-fold increase in those with modifiable risk factors such as atrial fibrillation and hypertension. Finally, the model was validated on the diverse All of Us dataset (AUROC = 0.764), highlighting ancestry and population differences while demonstrating effective generalization. This study introduces a predictive framework that identifies high-risk individuals and informs targeted prevention strategies, offering potential as a clinical decision-support tool.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658481","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}

{"title":"Impact of halogenation on scaffold toxicity assessed using HD-GEM machine learning model.","authors":"Bharath Reddy Boya, Jin-Hyung Lee, Jae-Mun Choi, Jintae Lee","doi":"10.1093/bib/bbaf347","DOIUrl":"https://doi.org/10.1093/bib/bbaf347","url":null,"abstract":"<p><p>Halogens play a fundamental role in drug design, influencing bioactivity, stability, and selectivity. However, their impact on toxicity, particularly genotoxicity, cardiotoxicity, and hepatotoxicity, remains a critical challenge in drug discovery. This study presents HD-GEM (Hybrid Dynamic Graph-based Ensemble Model), a novel machine learning framework integrating graph neural networks, descriptor-based molecular fingerprints, and ensemble meta-learning to predict the toxicity of halogenated aromatic compounds and drug scaffolds. HD-GEM demonstrates superior predictive power compared to conventional machine learning (ML) models and popular toxicity web applications like ProTox, ADMETlab, and admetSAR, achieving high accuracy and Receiver Operating Characteristic-Area Under Curve scores across diverse datasets. Importantly, a node perturbation analysis revealed that carbon, nitrogen, and oxygen atoms within the scaffold dominate toxicity predictions, whereas halogen contributions were minimal, challenging the conventional assumption that halogenation inherently increases toxicity in many pharmacological contexts. Among halogens, iodine-substituted compounds exhibit the lowest toxicity, a trend corroborated across single-, double-, and triple-ring scaffolds. Notably, polyhalogenated scaffolds show reduced toxicity, suggesting a stabilizing effect that mitigates reactive metabolite formation. This study presents an interpretable artificial intelligence-driven framework for toxicity prediction in the context of computational toxicology and cheminformatics. Atom-level and descriptor-based analyses reveal scaffold- and feature-specific contributions to toxicity.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658480","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}

{"title":"ADCNet: a unified framework for predicting the activity of antibody-drug conjugates.","authors":"Liye Chen, Biaoshun Li, Yihao Chen, Mujie Lin, Shipeng Zhang, Chenxin Li, Yu Pang, Ling Wang","doi":"10.1093/bib/bbaf228","DOIUrl":"10.1093/bib/bbaf228","url":null,"abstract":"<p><p>Antibody-drug conjugates (ADCs) have revolutionized the field of cancer treatment in the era of precision medicine due to their ability to precisely target cancer cells and release highly effective drugs. Nevertheless, the rational design and discovery of ADCs remain challenging because the relationship between their quintuple structures and activities is difficult to explore and understand. To address this issue, we first introduce a unified deep learning framework called ADCNet to explore such relationship and help design potential ADCs. The ADCNet highly integrates the protein representation learning language model ESM-2 and small-molecule representation learning language model functional group-based bidirectional encoder representations from transformers to achieve activity prediction through learning meaningful features from antigen and antibody protein sequences of ADC, SMILES strings of linker and payload, and drug-antibody ratio (DAR) value. Based on a carefully designed and manually tailored ADC data set, extensive evaluation results reveal that ADCNet performs best on the test set compared to baseline machine learning models across all evaluation metrics. For example, it achieves an average prediction accuracy of 87.12%, a balanced accuracy of 0.8689, and an area under receiver operating characteristic curve of 0.9293 on the test set. In addition, cross-validation, ablation experiments, and external independent testing results further prove the stability, advancement, and robustness of the ADCNet architecture. For the convenience of the community, we develop the first online platform (https://ADCNet.idruglab.cn) for the prediction of ADCs activity based on the optimal ADCNet model, and the source code is publicly available at https://github.com/idrugLab/ADCNet.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149285","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}

{"title":"PhosF3C: a feature fusion architecture with fine-tuned protein language model and conformer for prediction of general phosphorylation site.","authors":"Yuhuan Liu, Xueying Wang, Haitian Zhong, Jixiu Zhai, Xiaojuan Gong, Tianchi Lu","doi":"10.1093/bib/bbaf242","DOIUrl":"10.1093/bib/bbaf242","url":null,"abstract":"<p><p>Protein phosphorylation, a key post-translational modification, provides essential insight into protein properties, making its prediction highly significant. Using the emerging capabilities of large language models (LLMs), we apply Low-Rank Adaptation (LoRA) fine-tuning to ESM2, a powerful protein large language model, to efficiently extract features with minimal computational resources, optimizing task-specific text alignment. Additionally, we integrate the conformer architecture with the feature coupling unit to enhance local and global feature exchange, further improving prediction accuracy. Our model achieves state-of-the-art performance, obtaining area under the curve scores of 79.5%, 76.3%, and 71.4% at the S, T, and Y sites of the general data sets. Based on the powerful feature extraction capabilities of LLMs, we conduct a series of analyses on protein representations, including studies on their structure, sequence, and various chemical properties [such as hydrophobicity (GRAVY), surface charge, and isoelectric point]. We propose a test method called linear regression tomography which is a top-down method using representation to explore the model's feature extraction capabilities. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/PhosF3C.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149301","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}

{"title":"DECEPTICON: a correlation-based strategy for RNA-seq deconvolution inspired by a variation of the Anna Karenina principle.","authors":"Fulan Deng, Jiawei Zou, Miaochen Wang, Yida Gu, Jiale Wu, Lianchong Gao, Yuan Ji, Henry H Y Tong, Jie Chen, Wantao Chen, Lianjiang Tan, Yaoqing Chu, Xin Zou, Jie Hao","doi":"10.1093/bib/bbaf234","DOIUrl":"10.1093/bib/bbaf234","url":null,"abstract":"<p><p>Accurately deconvoluting cellular composition from bulk RNA-seq data is pivotal for understanding the tumor microenvironment and advancing precision medicine. Existing methods often struggle to consistently and accurately quantify cell types across heterogeneous RNA-seq datasets, particularly when ground truths are unavailable. In this study, we introduce DECEPTICON, a deconvolution strategy inspired by the Anna Karenina principle, which postulates that successful outcomes share common traits, while failures are more varied. DECEPTICON selects top-performing methods by leveraging correlations between different strategies and combines them dynamically to enhance performance. Our approach demonstrates superior accuracy in predicting cell-type proportions across multiple tumor datasets, improving correlation by 23.9% and reducing root mean square error by 73.5% compared to the best of 50 analyzed strategies. Applied to The Cancer Genome Atlas (TCGA) datasets for breast carcinoma, cervical squamous cell carcinoma, and lung adenocarcinoma, DECEPTICON-based predictions showed improved differentiation between patient prognoses. This correlation-based strategy offers a reliable, flexible tool for deconvoluting complex transcriptomic data and highlights its potential in refining prognostic assessments in oncology and advancing cancer biology.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149290","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}