Briefings in bioinformatics最新文献_第6页

Denoising spatially resolved transcriptomics with consistency of heterogeneous spatial coordinates, transcription, and morphology. 去噪空间分解转录组与一致性异质空间坐标，转录，和形态。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf528

Haiyue Wang, Peng Gao, Shaoqing Feng, Xiaoke Ma

{"title":"Denoising spatially resolved transcriptomics with consistency of heterogeneous spatial coordinates, transcription, and morphology.","authors":"Haiyue Wang, Peng Gao, Shaoqing Feng, Xiaoke Ma","doi":"10.1093/bib/bbaf528","DOIUrl":"10.1093/bib/bbaf528","url":null,"abstract":"Spatially resolved transcriptomics (SRT) simultaneously captures spatial coordinates, pathological features, and transcriptional profiles of cells within intact tissues, offering unprecedented opportunities to explore tissue architecture. However, SRT data often suffer from substantial technical noise introduced by experimental procedures, posing challenges for downstream analyses. To overcome these challenges, we introduce a Multiview Denoising framework for Spatial Transcriptomics (MvDST), which integrates a deep autoencoder and self-supervised learning to jointly reconstruct expression profiles, denoise features, and enforce cross-view consistency, effectively reducing technical noise, and heterogeneity. As a result, MvDST reliably and accurately delineates tissue subgroups across simulated datasets under various perturbations. In real cancer datasets, it distinguishes tumor-associated domains, identifies region-specific marker genes, and reveals intra-tumoral heterogeneity. Furthermore, we validate the robustness of MvDST across multiple spatial transcriptomics platforms, including 10 $times $ Visium, STARmap, and osmFISH. Overall, these results demonstrate that MvDST can serve as a crucial initial step for the analysis of spatially resolved transcriptomics data.","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/PMC12496014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228477","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

ISENICS: a model for identifying senescent immune cells and samples and characterization of their roles in tumor microenvironment. ISENICS：一个识别衰老免疫细胞和样本并表征其在肿瘤微环境中的作用的模型。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf469

Miaomiao Tian, Hao Cui, Xinyu Wang, Huading Hu, Longlong Dong, Song Xiao, Changfan Qu, Peng Wang, Hui Zhi, Shangwei Ning, Yue Gao

{"title":"ISENICS: a model for identifying senescent immune cells and samples and characterization of their roles in tumor microenvironment.","authors":"Miaomiao Tian, Hao Cui, Xinyu Wang, Huading Hu, Longlong Dong, Song Xiao, Changfan Qu, Peng Wang, Hui Zhi, Shangwei Ning, Yue Gao","doi":"10.1093/bib/bbaf469","DOIUrl":"10.1093/bib/bbaf469","url":null,"abstract":"Senescent immune cells secrete varied inflammatory factors that weaken the systemic anti-tumor ability and promote the proliferation and metastasis of tumor cells. Tumor cells could also accelerate the immune cellular senescence through diverse mechanisms. However, there has been a lack of indicators to quantify the senescence levels of different immune cell types. A model for Identifying Senescent Immune Cells and Samples was developed to explore the role of senescent immune cells in the tumor immune microenvironment (TIME). By integrating bulk and single-cell RNA-seq data, we constructed immune cell gene expression profiles for 23 cancer types using a deconvolution algorithm. By calculating the cellular senescence scores, we found that tumor samples exhibited higher senescence levels than normal samples. Monocytes/macrophages were prone to co-senescence with other cell subtypes. Differentially expressed genes in the high- and low-immune cellular senescence scores groups were enriched in the senescence pathway. Patients with higher levels of immunosenescence were associated with better prognosis. At the single-cell level, the number and strength of cell-to-cell interactions increased following immune cellular senescence in most cancers. Samples with senescent immune cells exhibited poorer immunotherapy response. Our study advances our understanding of senescent immune cells in the TIME, provides insights into cancer-specific relationships between immune cellular senescence and immune characteristics, and offers a model for identifying these senescent immune cells.","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/PMC12423394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085161","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

Mapping cancer heterogeneity: a consensus network approach to subtypes and pathways. 绘制癌症异质性：对亚型和途径的共识网络方法。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf452

Geng-Ming Hu, Hsin-Wei Chen, Chi-Ming Chen

{"title":"Mapping cancer heterogeneity: a consensus network approach to subtypes and pathways.","authors":"Geng-Ming Hu, Hsin-Wei Chen, Chi-Ming Chen","doi":"10.1093/bib/bbaf452","DOIUrl":"10.1093/bib/bbaf452","url":null,"abstract":"We introduce consensus MSClustering, an unsupervised hierarchical network approach that integrates multi-omics data to identify molecular subtypes and conserved pathways across diverse cancers. Using a novel heterogeneity index, we selected 167 key genes with functionally coherent roles validated through Gene Ontology analysis. Applied to 2439 tumors spanning 10 cancer types-and successfully extended to 2675 tumors (12 types) including cases with incomplete molecular data-MSClustering demonstrated: (i) precise classification of major cancer types and breast cancer molecular subtypes; (ii) discovery of novel pan-cancer squamous metaplastic signatures; (iii) exceptional prognostic stratification (log-rank P = 2.3 × 10-46); and (iv) superior performance over existing methods (COCA/SNF) in classification accuracy, cluster robustness, and computational efficiency. The method's multi-scale architecture uniquely resolves breast cancer heterogeneity across biological resolution levels. Pathway analysis further revealed four key oncogenic programs-proteoglycan signaling, chromosomal stability, VEGF-mediated angiogenesis, and drug metabolism-along with disruptions in immune and digestive system functions. This integrative framework marks a significant advancement in cancer genomics by enabling more refined molecular classification, enhanced prognostic insights, and deeper understanding of disease mechanisms. These results highlight the potential of MSClustering to inform the development of clinically relevant biomarkers and support more personalized strategies in precision oncology.","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/PMC12409415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144991302","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

A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies. 深度学习用于药物靶标结合预测的研究综述：模型、基准、评估和案例研究。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf491

Kusal Debnath, Pratip Rana, Preetam Ghosh

{"title":"A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies.","authors":"Kusal Debnath, Pratip Rana, Preetam Ghosh","doi":"10.1093/bib/bbaf491","DOIUrl":"10.1093/bib/bbaf491","url":null,"abstract":"Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug-target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug discovery research since researchers started using deep learning as a potent tool for DTB prediction. In particular, we examine how methodologies have evolved, starting with early heterogeneous network-based approaches, progressing to graph-based approaches that were widely accepted, followed by modern attention-based architectures, and finally, the most recent multimodal approaches. We also provide case studies utilizing an extensive compound library against specific protein targets implicated in critical cancer pathways to demonstrate the usefulness of these approaches. In addition to summarizing the latest developments in DTB prediction models, this review also identifies their drawbacks. It also highlights the outlook for the DTB prediction domain and future research directions. Combined, these studies present a more comprehensive view of how deep learning offers a quantitative framework for researching drug-target relationships, speeding up the identification of new drug candidates and making it easier to identify possible DTBs.","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/PMC12451107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111925","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

Correction to: Alpha_Mesh_Swc: automatic and robust surface mesh generation from the skeleton description of brain cells. Alpha_Mesh_Swc：根据脑细胞的骨架描述自动生成鲁棒的表面网格。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf525

引用次数: 0

soFusion: facilitating tissue structure identification via spatial multi-omics data fusion. soFusion：通过空间多组学数据融合促进组织结构识别。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf513

Na Yu, Wenrui Li, Xue Sun, Jing Hu, Qi Zou, Zhiping Liu, Daoliang Zhang, Wei Zhang, Rui Gao

{"title":"soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.","authors":"Na Yu, Wenrui Li, Xue Sun, Jing Hu, Qi Zou, Zhiping Liu, Daoliang Zhang, Wei Zhang, Rui Gao","doi":"10.1093/bib/bbaf513","DOIUrl":"10.1093/bib/bbaf513","url":null,"abstract":"The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decoders are designed to preserve the unique information embedded in each omics type. We evaluated soFusion on multiple datasets including gene expression, protein expression, and epigenetic features. Across all benchmarks, soFusion consistently outperformed existing methods in delineating anatomical structures and identifying spatial domains with improved continuity and reduced noise. Collectively, soFusion offers an effective solution for spatial multi-omics integration, substantially enhancing the robustness of spatial domain identification.","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/PMC12477611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184505","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

SurvBoard: standardized benchmarking for multi-omics cancer survival models. SurvBoard：多组学癌症生存模型的标准化基准。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf521

David Wissel, Nikita Janakarajan, Aayush Grover, Enrico Toniato, Maria Rodríguez Martínez, Valentina Boeva

{"title":"SurvBoard: standardized benchmarking for multi-omics cancer survival models.","authors":"David Wissel, Nikita Janakarajan, Aayush Grover, Enrico Toniato, Maria Rodríguez Martínez, Valentina Boeva","doi":"10.1093/bib/bbaf521","DOIUrl":"10.1093/bib/bbaf521","url":null,"abstract":"Multi-omics data, which include genomic, transcriptomic, epigenetic, and proteomic data, are gaining increasing importance for determining the clinical outcomes of cancer patients. Several recent studies have evaluated various multimodal integration strategies for cancer survival prediction, highlighting the need for standardizing model performance results. Addressing this issue, we introduce SurvBoard, a benchmark framework that standardizes key experimental design choices. SurvBoard enables comparisons between single-cancer and pan-cancer data models and assesses the benefits of using patient data with missing modalities. We also address common pitfalls in preprocessing and validating multi-omics cancer survival models. We apply SurvBoard to several exemplary use cases, further confirming that statistical models tend to outperform deep learning methods, especially for metrics measuring survival function calibration. Moreover, most models exhibit better performance when trained in a pan-cancer context and can benefit from leveraging samples for which data of some omics modalities are missing. We provide a web service for model evaluation and to make our benchmark results easily accessible and viewable: https://www.survboard.science/. All code is available on GitHub: https://github.com/BoevaLab/survboard/. All benchmark outputs are available on Zenodo: 10.5281/zenodo.11066226. A video tutorial on how to use the Survboard leaderboard is available on YouTube at https://youtu.be/HJrdpJP8Vvk.","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/PMC12486238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198432","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

MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model. MegSite：基于多模态蛋白语言模型的核酸结合残基精确预测方法。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf524

Feng Hu, Wenwu Zeng, Shaoliang Peng

{"title":"MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model.","authors":"Feng Hu, Wenwu Zeng, Shaoliang Peng","doi":"10.1093/bib/bbaf524","DOIUrl":"10.1093/bib/bbaf524","url":null,"abstract":"Accurate identification of nucleic acid-binding residues is crucial for understanding protein-nucleic acid interactions, which play a key role in gene expression research and the discovery of regulatory mechanisms. Despite numerous computational efforts to address this challenge, achieving high accuracy remains difficult due to the complexity of extracting meaningful insights from proteins. Here, we introduce MegSite, a novel multimodal protein language model-informed method that integrates discriminative knowledge from protein sequence, structure, and function. This work presents the first integration of ESM3 multimodal features for nucleic acid-binding site prediction. MegSite significantly outperforms existing prediction methods, as evidenced by its performance on multiple independent test sets. The Matthews correlation coefficient values achieved by MegSite on DNA-129_Test, DNA-181_Test, RNA-117_Test, and RNA-285_Test are 0.567, 0.444, 0.411, and 0.421, representing the improvements of 2.72%, 7.66%, 1.22% and 6.58% over the second-best method separately. Notably, MegSite demonstrates robust performance even on proteins with low structural similarity, surpassing the previous structure-based methods. Furthermore, this method is seamlessly extendable to the predicted protein structure and a newly released RNA-binding residue test set with high accuracy, highlighting its broad applicability. Comprehensive experimental results reveal that the superior performance of MegSite is attributed to its effective integration of multimodal protein knowledge.","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/PMC12496013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228447","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

Multi-view gene panel characterization for spatially resolved omics. 空间分辨组学的多视图基因面板表征。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf478

Daniel Kim, Wenze Ding, Akira Nguyen Shaw, Marni Torkel, Cameron J Turtle, Pengyi Yang, Jean Yang

{"title":"Multi-view gene panel characterization for spatially resolved omics.","authors":"Daniel Kim, Wenze Ding, Akira Nguyen Shaw, Marni Torkel, Cameron J Turtle, Pengyi Yang, Jean Yang","doi":"10.1093/bib/bbaf478","DOIUrl":"10.1093/bib/bbaf478","url":null,"abstract":"Spatially resolved transcriptomics has revolutionized the study of complex tissues by enabling cellular and subcellular resolution. However, targeted spatial technologies depend on pre-selected gene panels, which are typically curated based on prior biological knowledge or specific research hypotheses. While existing methods often focus on optimizing for cell type identification, we argue that effective panel design should also account for transcriptional variation, pathway-level coverage, and minimal gene redundancy. To meet these broader criteria, we developed a two-part framework: (i) panelScope, a gene panel characterization platform that characterizes panels from multiple perspectives, allowing for holistic comparisons of gene panels for custom panel design; and (ii) panelScope-OA, a genetic algorithm that integrates these characterization metrics into a multi-loss function to automate panel optimization. We applied panelScope and panelScope-OA to characterize nine panels across four datasets. Notably, computationally constructed gene panels performed competitively in capturing major cell types when compared to our in-house manually curated panel. However, refined manual curation offered distinct advantages, particularly in capturing minor cell types. Our results demonstrate the utility of panelScope and panelScope-OA by offering quantitative and multi-dimensional insights to support the design of panels tailored to diverse research needs.","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/PMC12495993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228472","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 toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction. 药物发现中的计算毒理学：人工智能在ADMET和毒性预测中的应用。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf533

Jiangyan Zhang, Haolin Li, Yuncong Zhang, Junyang Huang, Liping Ren, Chuantao Zhang, Quan Zou, Yang Zhang

{"title":"Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.","authors":"Jiangyan Zhang, Haolin Li, Yuncong Zhang, Junyang Huang, Liping Ren, Chuantao Zhang, Quan Zou, Yang Zhang","doi":"10.1093/bib/bbaf533","DOIUrl":"10.1093/bib/bbaf533","url":null,"abstract":"Toxicity risk assessment plays a crucial role in determining the clinical success and market potential of drug candidates. Traditional animal-based testing is costly, time-consuming, and ethically controversial, which has led to the rapid development of computational toxicology. This review surveys over 20 ADMET prediction platforms, categorizing them into rule/statistical-based methods, machine learning (ML) methods, and graph-based methods. We also summarize major toxicological databases into four types: chemical toxicity, environmental toxicology, alternative toxicology, and biological toxin databases, highlighting their roles in model training and validation. Furthermore, we review recent advancements in ML and artificial intelligence (AI) applied to toxicity prediction, covering acute toxicity, organ-specific toxicities, and carcinogenicity. The field is transitioning from single-endpoint predictions to multi-endpoint joint modeling, incorporating multimodal features. We also explore the application of generative modeling techniques and interpretability frameworks to improve the accuracy and credibility of predictions. Additionally, we discuss the use of network toxicology in evaluating the safety of traditional Chinese medicines (TCMs) and the potential of large language models (LLMs) in literature mining, knowledge integration, and molecular toxicity prediction. Finally, we address current challenges, including data quality, model interpretability, and causal inference, and propose future directions such as multi-omics integration, interpretable AI models, and domain-specific LLMs, aiming to provide more efficient and precise technical support for preclinical toxicity assessments in drug development.","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/PMC12499773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238038","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