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Leveraging attention-based deep multiple instance and multiple task learning for improved neoepitope identification. 利用基于注意力的深度多实例和多任务学习改进新表位识别。
IF 7.7
Cell systems Pub Date : 2025-10-06 DOI: 10.1016/j.cels.2025.101404
Wei Qu, Shanfeng Zhu
{"title":"Leveraging attention-based deep multiple instance and multiple task learning for improved neoepitope identification.","authors":"Wei Qu, Shanfeng Zhu","doi":"10.1016/j.cels.2025.101404","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101404","url":null,"abstract":"<p><p>Accurate prediction of major histocompatibility complex class I (MHC class I) neoepitopes is crucial for personalized cancer immunotherapy. Current methods struggle with predicting ligand presentation for multiple alleles and identifying neoepitopes. We introduce NeoMHCI, a deep learning model that combines attention-based multiple instance learning (MIL) and multi-task learning for precise MHC class I neoepitope identification. NeoMHCI uses MIL to generate high-quality peptide embeddings with multiple MHC class I molecules and enhances immunogenicity prioritization through fine-tuning. Analyses on benchmark datasets show NeoMHCI outperforms existing methods, achieving an area under the receiver operating characteristic curve of 0.948 and an area under the precision-recall curve of 0.496 on unobserved multi-allele ligand presentation prediction and the highest top-5 accuracy (42.3%) for neoepitope recognition, indicating potential for personalized vaccines and therapies. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101404"},"PeriodicalIF":7.7,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245880","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
Comprehensive genetic interaction analysis of the Bacillus subtilis envelope using double-CRISPRi. 利用双crispri对枯草芽孢杆菌包膜进行综合遗传互作分析。
IF 7.7
Cell systems Pub Date : 2025-10-03 DOI: 10.1016/j.cels.2025.101406
Byoung-Mo Koo, Horia Todor, Jiawei Sun, Jordi van Gestel, John S Hawkins, Cameron C Hearne, Amy B Banta, Kerwyn Casey Huang, Jason M Peters, Carol A Gross
{"title":"Comprehensive genetic interaction analysis of the Bacillus subtilis envelope using double-CRISPRi.","authors":"Byoung-Mo Koo, Horia Todor, Jiawei Sun, Jordi van Gestel, John S Hawkins, Cameron C Hearne, Amy B Banta, Kerwyn Casey Huang, Jason M Peters, Carol A Gross","doi":"10.1016/j.cels.2025.101406","DOIUrl":"10.1016/j.cels.2025.101406","url":null,"abstract":"<p><p>Understanding bacterial gene function remains a major challenge. Double-mutant genetic interaction analysis addresses this challenge by uncovering the functional partners of targeted genes, enabling association of genes of unknown function with known pathways and unraveling of connections among well-studied pathways, but such approaches are difficult to implement at the genome scale. Here, we use double-CRISPR interference (CRISPRi) to systematically quantify genetic interactions at scale for the Bacillus subtilis cell envelope, including essential genes. We discover >1,000 genetic interactions, some known and others novel. Our analysis pipeline and experimental follow-ups reveal the shared and distinct roles of paralogous genes such as mreB and mbl in peptidoglycan and teichoic acid synthesis and identify additional genes involved in the well-studied process of cell division. Overall, our study provides valuable insights into gene function and demonstrates the utility of double-CRISPRi for high-throughput dissection of bacterial gene networks, providing a blueprint for future studies in diverse species. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101406"},"PeriodicalIF":7.7,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228402","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
Dual CRISPRi-seq for genome-wide genetic interaction studies identifies key genes involved in the pneumococcal cell cycle. 用于全基因组遗传相互作用研究的双crispr -seq鉴定了参与肺炎球菌细胞周期的关键基因。
IF 7.7
Cell systems Pub Date : 2025-10-03 DOI: 10.1016/j.cels.2025.101408
Julien Dénéréaz, Elise Eray, Bimal Jana, Vincent de Bakker, Horia Todor, Tim van Opijnen, Xue Liu, Jan-Willem Veening
{"title":"Dual CRISPRi-seq for genome-wide genetic interaction studies identifies key genes involved in the pneumococcal cell cycle.","authors":"Julien Dénéréaz, Elise Eray, Bimal Jana, Vincent de Bakker, Horia Todor, Tim van Opijnen, Xue Liu, Jan-Willem Veening","doi":"10.1016/j.cels.2025.101408","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101408","url":null,"abstract":"<p><p>Uncovering genotype-phenotype relationships is hampered by genetic redundancy. For example, most genes in Streptococcus pneumoniae are non-essential under laboratory conditions. A powerful approach to unravel genetic redundancy is by identifying gene-gene interactions. We developed a broadly applicable dual CRISPRi-seq method and analysis pipeline to probe genetic interactions (GIs) genome-wide. A library of 869 dual single-guide RNAs (sgRNAs) targeting high-confidence operons was created, covering over 70% of the genetic elements in the pneumococcal genome. Testing these 378,015 unique combinations, 4,026 significant GIs were identified. Besides known GIs, we found previously unknown positive and negative interactions involving genes in fundamental cellular processes such as division and chromosome segregation. The presented methods and bioinformatic approaches can serve as a roadmap for genome-wide gene interaction studies in other organisms. All interactions are available for exploration via the Pneumococcal Genetic Interaction Network (PneumoGIN), which can serve as a starting point for new biological discoveries. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101408"},"PeriodicalIF":7.7,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228624","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
Protein surface chemistry encodes an adaptive tolerance to desiccation. 蛋白质表面化学编码对干燥的适应性耐受性。
IF 7.7
Cell systems Pub Date : 2025-10-02 DOI: 10.1016/j.cels.2025.101407
Paulette Sofía Romero-Pérez, Haley M Moran, David P Cordone, Azeem Horani, Alexander Truong, Edgar Manriquez-Sandoval, John F Ramirez, Alec Martinez, Edith Gollub, Kara Hunter, Kavindu C Kolamunna, Jeffrey M Lotthammer, Ryan J Emenecker, Hui Liu, Janet H Iwasa, Thomas C Boothby, Alex S Holehouse, Stephen D Fried, Shahar Sukenik
{"title":"Protein surface chemistry encodes an adaptive tolerance to desiccation.","authors":"Paulette Sofía Romero-Pérez, Haley M Moran, David P Cordone, Azeem Horani, Alexander Truong, Edgar Manriquez-Sandoval, John F Ramirez, Alec Martinez, Edith Gollub, Kara Hunter, Kavindu C Kolamunna, Jeffrey M Lotthammer, Ryan J Emenecker, Hui Liu, Janet H Iwasa, Thomas C Boothby, Alex S Holehouse, Stephen D Fried, Shahar Sukenik","doi":"10.1016/j.cels.2025.101407","DOIUrl":"10.1016/j.cels.2025.101407","url":null,"abstract":"<p><p>Cellular desiccation-the loss of nearly all water from the cell-is a recurring stress that drives widespread protein dysfunction. To survive, part of the proteome must resume function upon rehydration. Which proteins tolerate desiccation, and the molecular determinants that underlie this tolerance, are largely unknown. Here, we use quantitative mass spectrometry and structural proteomics to show that certain proteins possess an innate capacity to tolerate extreme water loss. Structural analyses point to protein surface chemistry as a key determinant of desiccation tolerance, which we test by showing that rational surface mutants can convert a desiccation-sensitive protein into a tolerant one. We also find that highly tolerant proteins are responsible for the production of small-molecule building blocks, while intolerant proteins are involved in energy-consuming processes such as ribosome biogenesis. We propose that this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101407"},"PeriodicalIF":7.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226498","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
Learning cell-specific networks from dynamics and geometry of single cells. 从单个细胞的动力学和几何结构中学习细胞特异性网络。
IF 7.7
Cell systems Pub Date : 2025-10-01 DOI: 10.1016/j.cels.2025.101399
Stephen Y Zhang, Michael P H Stumpf
{"title":"Learning cell-specific networks from dynamics and geometry of single cells.","authors":"Stephen Y Zhang, Michael P H Stumpf","doi":"10.1016/j.cels.2025.101399","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101399","url":null,"abstract":"<p><p>Cell dynamics and biological function are governed by intricate networks of molecular interactions. Inferring these interactions from data is a notoriously difficult inverse problem. Most existing network inference methods construct population-averaged representations of gene interaction networks, and they do not naturally allow us to infer differences in interaction activity across heterogeneous cell populations. We introduce locaTE, an information theoretic approach that leverages single-cell, dynamical information, together with geometry of the cell-state manifold, to infer cell-specific, causal gene interaction networks in a manner that is agnostic to the topology of the underlying biological trajectory. Through extensive simulation studies and applications to experimental datasets spanning mouse primitive endoderm formation, pancreatic development, and hematopoiesis, we demonstrate superior performance and the generation of additional insights, compared with standard population-averaged inference methods. We find that locaTE provides a powerful network inference method that allows us to distil cell-specific networks from single-cell data. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101399"},"PeriodicalIF":7.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214592","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
Identifying an optimal perturbation to induce a desired cell state by generative deep learning. 通过生成式深度学习识别最佳扰动以诱导所需的细胞状态。
IF 7.7
Cell systems Pub Date : 2025-09-24 DOI: 10.1016/j.cels.2025.101405
Younghyun Han, Hyunjin Kim, Chun-Kyung Lee, Kwang-Hyun Cho
{"title":"Identifying an optimal perturbation to induce a desired cell state by generative deep learning.","authors":"Younghyun Han, Hyunjin Kim, Chun-Kyung Lee, Kwang-Hyun Cho","doi":"10.1016/j.cels.2025.101405","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101405","url":null,"abstract":"<p><p>Controlling cell states is pivotal in biological research, yet understanding the specific perturbations that induce desired changes remains challenging. To address this, we present PAIRING (perturbation identifier to induce desired cell states using generative deep learning), which identifies cellular perturbations leading to the desired cell state. PAIRING embeds cell states in the latent space and decomposes them into basal states and perturbation effects. The identification of optimal perturbations is achieved by comparing the decomposed perturbation effects with the vector representing the transition toward the desired cell state in the latent space. We demonstrate that PAIRING can identify perturbations transforming given cell states into desired states across different types of transcriptome datasets. PAIRING is employed to identify perturbations that lead colorectal cancer cells to a normal-like state. Moreover, simulating gene expression changes using PAIRING provides mechanistic insights into the perturbation. We anticipate that it will have a broad impact on therapeutic development, potentially applicable across various biological domains.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101405"},"PeriodicalIF":7.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152170","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
Accelerated design of Escherichia coli reduced genomes using a whole-cell model and machine learning. 利用全细胞模型和机器学习加速设计大肠杆菌减少基因组。
IF 7.7
Cell systems Pub Date : 2025-09-24 DOI: 10.1016/j.cels.2025.101392
Ioana M Gherman, Kieren Sharma, Joshua Rees-Garbutt, Wei Pang, Zahraa S Abdallah, Thomas E Gorochowski, Claire S Grierson, Lucia Marucci
{"title":"Accelerated design of Escherichia coli reduced genomes using a whole-cell model and machine learning.","authors":"Ioana M Gherman, Kieren Sharma, Joshua Rees-Garbutt, Wei Pang, Zahraa S Abdallah, Thomas E Gorochowski, Claire S Grierson, Lucia Marucci","doi":"10.1016/j.cels.2025.101392","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101392","url":null,"abstract":"<p><p>Whole-cell models (WCMs) are multi-scale computational models that aim to simulate the function of all genes and processes within a cell. This approach is promising for designing genomes tailored for specific tasks. However, a limitation of WCMs is their long runtime. Here, we show how machine learning (ML) surrogates can be used to address this limitation by training them on WCM data to accurately predict cell division. Our ML surrogate achieves a 95% reduction in computational time compared with the original WCM. We then show that the surrogate and a genome-design algorithm can generate an in silico-reduced E. coli cell, where 40% of the genes included in the WCM were removed. The reduced genome is validated using the WCM and interpreted biologically using Gene Ontology analysis. This approach illustrates how the holistic understanding gained from a WCM can be leveraged for synthetic biology tasks while reducing runtime. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101392"},"PeriodicalIF":7.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152209","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
Cell-free recombinase-integrated Boolean output system. 无细胞重组集成布尔输出系统。
IF 7.7
Cell systems Pub Date : 2025-09-23 DOI: 10.1016/j.cels.2025.101395
Jingyao Chen, Aviva Borison, Douglas Densmore, Wilson W Wong
{"title":"Cell-free recombinase-integrated Boolean output system.","authors":"Jingyao Chen, Aviva Borison, Douglas Densmore, Wilson W Wong","doi":"10.1016/j.cels.2025.101395","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101395","url":null,"abstract":"<p><p>Cell-free gene expression systems are increasingly important in fundamental research and biomanufacturing, offering a versatile platform for studying gene circuits and biocomputation. We present the cell-free recombinase-integrated Boolean output system (CRIBOS), a site-specific recombinase-based multiplex genetic circuit platform designed for cell-free environments. With CRIBOS, we built over 20 multi-input-multi-output circuits, including 2-input-2-output genetic circuits and a 2-input-4-output decoder. Combined with allosteric transcription factor (aTF)-based sensors, the circuits demonstrate multiplex environmental sensing. Moreover, utilizing paper-based CRIBOS, which demonstrates remarkable portability and stability, we present a biological memory storage logic circuit device that can preserve DNA-based biological information for over 4 months with minimal resources, energy costs, and maintenance requirements. Implementing CRIBOS not only expands the application of multiplex Boolean logic gates from cellular systems to the cell-free environment but also augments their overall versatility, opening new avenues for designing and applying sophisticated genetic circuits. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101395"},"PeriodicalIF":7.7,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139790","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
Mild HIV-specific selective forces overlaying natural CD4+ T cell dynamics explain the clonality and decay dynamics of HIV reservoir cells. 轻微的HIV特异性选择力覆盖自然CD4+ T细胞动力学解释了HIV储存库细胞的克隆和衰变动力学。
IF 7.7
Cell systems Pub Date : 2025-09-22 DOI: 10.1016/j.cels.2025.101402
Daniel B Reeves, Danielle N Rigau, Arianna Romero, Hao Zhang, Francesco R Simonetti, Joseph Varriale, Rebecca Hoh, Li Zhang, Kellie N Smith, Luis J Montaner, Leah H Rubin, Stephen J Gange, Nadia R Roan, Phyllis C Tien, Joseph B Margolick, Michael J Peluso, Steven G Deeks, Joshua T Schiffer, Janet D Siliciano, Robert F Siliciano, Annukka A R Antar
{"title":"Mild HIV-specific selective forces overlaying natural CD4+ T cell dynamics explain the clonality and decay dynamics of HIV reservoir cells.","authors":"Daniel B Reeves, Danielle N Rigau, Arianna Romero, Hao Zhang, Francesco R Simonetti, Joseph Varriale, Rebecca Hoh, Li Zhang, Kellie N Smith, Luis J Montaner, Leah H Rubin, Stephen J Gange, Nadia R Roan, Phyllis C Tien, Joseph B Margolick, Michael J Peluso, Steven G Deeks, Joshua T Schiffer, Janet D Siliciano, Robert F Siliciano, Annukka A R Antar","doi":"10.1016/j.cels.2025.101402","DOIUrl":"10.1016/j.cels.2025.101402","url":null,"abstract":"<p><p>To determine whether HIV persistence arises from the natural dynamics of memory (m)CD4+ T cells, we compare clonal dynamics of HIV proviruses and mCD4+ T cells from the same people living with HIV (PWH) on antiretroviral therapy and from matched HIV-seronegative people (N = 51). HIV proviruses are more clonal than mCD4+ T cells but similarly clonal to antigen-specific cells. Increasing reservoir clonality over time and differential decay of intact and defective proviruses are not explained by mCD4+ T cell kinetics alone. We develop and validate a stochastic model trained on 10 quantitative data metrics, which shows that negative selection against HIV-infected cells is necessary to explain all metrics. We estimate the strength of negative selection, finding that death of cells harboring intact and defective proviruses is infrequently (∼6% and ∼2% on average) due to HIV-specific factors. Thus, our data indicate that HIV persistence is mostly, but not entirely, driven by natural mCD4+ kinetics.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101402"},"PeriodicalIF":7.7,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133052","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
Weakly supervised peptide-TCR binding prediction facilitates neoantigen identification. 弱监督肽- tcr结合预测有助于新抗原鉴定。
IF 7.7
Cell systems Pub Date : 2025-09-22 DOI: 10.1016/j.cels.2025.101403
Yuli Gao, Yicheng Gao, Siqi Wu, Danlu Li, Chi Zhou, Fangliangzi Meng, Kejing Dong, Xueying Zhao, Ping Li, Aibin Liang, Qi Liu
{"title":"Weakly supervised peptide-TCR binding prediction facilitates neoantigen identification.","authors":"Yuli Gao, Yicheng Gao, Siqi Wu, Danlu Li, Chi Zhou, Fangliangzi Meng, Kejing Dong, Xueying Zhao, Ping Li, Aibin Liang, Qi Liu","doi":"10.1016/j.cels.2025.101403","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101403","url":null,"abstract":"<p><p>The identification of T cell neoantigens is fundamental and computationally challenging in tumor immunotherapy study. Current prediction methods mainly focus on peptide properties, human leukocyte antigen (HLA) binding affinity, or single peptide-major histocompatibility complex-T cell receptor (pMHC-TCR) interactions, often overlooking the patient-specific TCR profile in evaluating neoantigen immunogenicity. This limited scope has constrained the performance and application of these tools in real-world settings for neoantigen identification. To address these limitations, we developed \"TCRBagger,\" a weakly supervised learning framework that uses the bagging of sample-specific TCR profiles to enhance personalized neoantigen identification. TCRBagger integrates three learning strategies-self-supervised, denoising, and multi-instance learning (MIL)-for modeling peptide-TCR binding to identify immunogenic neoantigens. Our comprehensive tests and applications reveal that TCRBagger outperforms existing tools by modeling peptide-TCR profile interactions, accordingly enhancing the capability of immunogenic neoantigen identification. Collectively, TCRBagger provides an unprecedented perspective and methodology for modeling the interaction between a peptide and patient-specific TCR profiles, facilitating neoantigen identification for personalized tumor immunotherapy. A record of this paper's Transparent Peer Review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101403"},"PeriodicalIF":7.7,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133121","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
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