Briefings in bioinformatics最新文献_第4页

A flexible framework for robust and efficient Mendelian randomization with debiasing. 一个灵活的框架稳健和有效的孟德尔随机化与去偏。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag468

Linsui Deng, Kejun He, Xianyang Zhang

{"title":"A flexible framework for robust and efficient Mendelian randomization with debiasing.","authors":"Linsui Deng, Kejun He, Xianyang Zhang","doi":"10.1093/bib/bbag468","DOIUrl":"10.1093/bib/bbag468","url":null,"abstract":"Mendelian randomization (MR) has been widely used to infer causal relationships between exposures and outcomes in epidemiological studies. However, classical MR assumptions can be violated when genetic variants are associated with outcomes through pathways other than the exposure, leading to uncorrelated and/or correlated pleiotropy. Additionally, measurement error arising from the inherent uncertainty in summary statistics obtained from large-scale genome-wide association studies can introduce bias into the causal effect estimate. To address these issues, we develop a debiased mixture inverse variance weighting ($mathsf{dmIVW}$) method with three major advantages. First, it is capable of simultaneously handling various types of pleiotropy and eliminating the bias caused by uncertainty. Second, it can guard against distortion caused by invalid genetic variants while effectively harnessing their information. Third, our unified framework facilitates a fair comparison and combination of a series of submodels, encompassing several popular MR methods as special cases. Through real data applications, the effectiveness and robustness of $mathsf{dmIVW}$ in estimating the causal effects of risk factors on common diseases are demonstrated.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900454","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

Do papers tell the whole story? A benchmark and framework for uncovering hidden implementation gaps in bioinformatics. 报纸会讲述整个故事吗？揭示生物信息学中隐藏的实施差距的基准和框架。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag509

Tianxiang Xu, Xiaoyan Zhu, Xin Lai, Xin Lian, Sizhe Dang, Hangyu Cheng, Jiayin Wang

{"title":"Do papers tell the whole story? A benchmark and framework for uncovering hidden implementation gaps in bioinformatics.","authors":"Tianxiang Xu, Xiaoyan Zhu, Xin Lai, Xin Lian, Sizhe Dang, Hangyu Cheng, Jiayin Wang","doi":"10.1093/bib/bbag509","DOIUrl":"10.1093/bib/bbag509","url":null,"abstract":"As bioinformatics software is increasingly applied across a broader range of scenarios and the rapid development of large language models (LLMs) further lowers the barriers to software use and development, the composition of the bioinformatics research community is undergoing substantial change. Consequently, a growing number of researchers require a deeper understanding of methodological details and software behavior. In this context, systematically analyzing the relationship between paper descriptions and code implementations is emerging as an important new challenge in the field. To address this challenge, we introduce paper-code consistency analysis as a new research perspective and construct BioCon, the first benchmark dataset for paper-code consistency analysis in bioinformatics. Furthermore, we develop a unified cross-modal analysis framework to systematically investigate this problem from three perspectives: sentence-level detection, cross-modal retrieval, and project-level assessment. Experimental results demonstrate that the proposed framework can effectively model the semantic relationships between scientific publications and software implementations. Further case studies reveal that paper-code inconsistency is not a single phenomenon but arises from multiple underlying causes, among which Author-Perceived Non-Essential Details represents the most prevalent category. These findings suggest that paper-code consistency analysis is not merely a technical problem but also raises broader discussions regarding knowledge dissemination, the boundaries of code disclosure, and community norms. We hope that this work will encourage the bioinformatics community to re-examine the relationship between scientific publications and software implementations while providing a foundation for future research in paper-code consistency analysis.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13584071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148988092","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

ToxMamba: a novel method for toxic peptide prediction based on the fusion of pretrained embeddings and multi-scale state space models. ToxMamba：一种基于预训练嵌入和多尺度状态空间模型融合的毒性肽预测新方法。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag516

Jingya Fang, Wenqi Shen, Yanru Li, Yu Jin, Haodong Li, Yuanyuan Chen, Zhihui Xu, Jinhu Huang, Cong Pian, Zutan Li

{"title":"ToxMamba: a novel method for toxic peptide prediction based on the fusion of pretrained embeddings and multi-scale state space models.","authors":"Jingya Fang, Wenqi Shen, Yanru Li, Yu Jin, Haodong Li, Yuanyuan Chen, Zhihui Xu, Jinhu Huang, Cong Pian, Zutan Li","doi":"10.1093/bib/bbag516","DOIUrl":"10.1093/bib/bbag516","url":null,"abstract":"Peptide therapeutics offer great promise in drug discovery, yet the accurate identification of peptide toxicity remains essential for both safety assessment and biodefense. We developed ToxMamba, a toxic peptide prediction framework that integrates Implicit Structure Model (ISM)-derived pretrained embeddings, state space models, multi-scale feature learning, and ensemble inference. Built upon the Mamba architecture, ToxMamba enables efficient sequence modeling and captures contextual patterns across multiple receptive fields from parallel branches. On the independent test set, ToxMamba consistently surpasses existing methods, improving the F1 score and Matthews Correlation Coefficient by 3.9-18.3 and 7.5-46.9 percentage points, respectively. Ablation analyses further showed that Mamba-based models generally achieved stronger overall performance than Transformer-, Convolutional Neural Network-Long Short-Term Memory-Attention-, and Kolmogorov-Arnold Network-based variants. Feature visualization showed that ISM embeddings yielded clearer separation between toxic and non-toxic peptides in the latent space. Intermediate-layer feature analysis highlighted residue-level regions associated with toxicity-related sequence motifs, supporting the biological interpretability of ToxMamba. Overall, this study provides an efficient and interpretable computational framework for high-throughput toxic peptide screening.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126572","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

ContiTE: continuous manifold MoE for few-shot cross-tissue mRNA translation efficiency prediction. 连续流形MoE用于小剂量跨组织mRNA翻译效率预测。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag518

Yuxiao Wei, Qi Zhang, Sainan Huo, Xuezhong Zhou

{"title":"ContiTE: continuous manifold MoE for few-shot cross-tissue mRNA translation efficiency prediction.","authors":"Yuxiao Wei, Qi Zhang, Sainan Huo, Xuezhong Zhou","doi":"10.1093/bib/bbag518","DOIUrl":"10.1093/bib/bbag518","url":null,"abstract":"Predicting mRNA translation efficiency across diverse tissues is a critical yet challenging task due to the phenomenon of concept shift, where identical genomic sequences exhibit distinct functional profiles across varying cellular environments. Existing static models are often limited by fixed parameterization, struggling to capture these continuous regulatory variations without requiring computationally expensive fine-tuning. To address these limitations, we propose ContiTE, a continuous manifold mixture-of-experts (MoE) framework explicitly designed for efficient few-shot cross-tissue adaptation. Unlike traditional MoE architectures that rely on discrete output-space mixing, ContiTE operates on a continuous parameter manifold by dynamically synthesizing domain-specific weights through a context-aware hyper-router that linearly combines shared atomic basis kernels. This paradigm enables smooth interpolation of translational rules and provides a flexible mechanism to model complex, context-dependent biological regulation. Furthermore, we introduce a gradient-based test-time adaptation strategy that allows the model to rapidly align to new, unseen tissues by solely optimizing low-dimensional context embeddings while keeping the backbone parameters frozen. Experimental results on a comprehensive human and mouse atlas demonstrate that ContiTE significantly outperforms state-of-the-art methods in few-shot scenarios, improving average Pearson correlation coefficient by 12.09% and $R^{2}$ by 18.22%. By mitigating negative transfer and requiring only limited target-domain data for calibration, ContiTE provides a computational framework for tissue-specific mRNA translation-efficiency prediction and demonstrates a parameter-reconstruction strategy that may be extensible to other cross-domain sequence-learning tasks, subject to task-specific validation.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011927","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

ToxiCompass: an evidence-tracked benchmark for short-peptide toxicity screening and confidence-aware assay prioritization. 毒物罗盘：短肽毒性筛选和信心意识测定优先级的证据跟踪基准。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag526

Qiule Yu, Zhixing Zhang, Weihua Li, Guixia Liu, Yun Tang

{"title":"ToxiCompass: an evidence-tracked benchmark for short-peptide toxicity screening and confidence-aware assay prioritization.","authors":"Qiule Yu, Zhixing Zhang, Weihua Li, Guixia Liu, Yun Tang","doi":"10.1093/bib/bbag526","DOIUrl":"10.1093/bib/bbag526","url":null,"abstract":"Binary peptide-toxicity predictors support early screening but do not indicate which assays should follow a toxin-like prediction. We present ToxiCompass, an evidence-tracked benchmark and two-stage protocol for short-peptide toxicity screening and assay-family prioritization. The benchmark contains 6114 peptides reconstructed from online database of biotoxins and reviewed Universal Protein Knowledgebase records, including curated toxin-positive peptides and length-matched operational comparators. Among 2866 Stage 2 candidate toxin-positive peptides, 2858 retained at least one observed-positive target/effect-family label after evidence review. Exact-sequence merging, provenance tracking, Many-against-Many sequence searching 2 clustering at 90% sequence identity (MMSeqs90) cluster-level splitting, and explicit evidence rules define the evaluation. Stage 1 assigns a toxin-like score relative to the operational-comparator population, whereas Stage 2 ranks 13 broad target/effect families to prioritize follow-up assays for toxin-positive peptides with retained evidence. On the primary MMSeqs90 cluster split, Stage 1 achieved an area under the receiver operating characteristic curve of 0.9871 and an area under the precision-recall curve of 0.9867, while Stage 2 achieved a Hit@3 of 0.8955. In a more conservative grouped-split sensitivity analysis, Stage 2 Hit@3 was 0.6506. Together, these results support ToxiCompass as a reproducible benchmark linking toxin-like peptide screening with evidence-aware prioritization of follow-up assay families.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149058491","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

Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist. 数据驱动的细胞亚型鉴定单细胞转录组数据与Subtypist。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag529

Yue Yao, Xin Shao, Renjie Chen, Jingyang Qian, Han Gao, Yiyang Peng, Jie Gao, Xiaohui Fan

{"title":"Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.","authors":"Yue Yao, Xin Shao, Renjie Chen, Jingyang Qian, Han Gao, Yiyang Peng, Jie Gao, Xiaohui Fan","doi":"10.1093/bib/bbag529","DOIUrl":"10.1093/bib/bbag529","url":null,"abstract":"Identification of new cell subtypes in single-cell RNA sequencing (scRNA-seq) data is critically crucial in accurately understanding the mechanisms of disease occurrence and development, which provides unprecedented insights into the development of therapeutic strategies, however, despite the abundance of existing methods, most heavily rely on the reference with the fixed cell labels, which fail to uncover new cell subtypes. Herein, we propose Subtypist, a fully data-driven multiscale iterative algorithm for cell subtype identification in scRNA-seq data without relying on external prior references. The benchmarking evaluation on both simulated and real datasets demonstrates its superior performance over other existing methods, and Subtypist was further applied to three disease scenarios, uncovering novel biologically meaningful cell subtypes and revealing their in-depth cell-cell communicative mechanisms underlying hepatocellular carcinoma, esophageal squamous cell carcinoma, and myocardial infarction. In summary, Subtypist enables the data-driven and reproducible identification of cell subtypes, providing an invaluable tool for accurately characterizing the cellular and molecular heterogeneity underlying disease pathogenesis.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149069203","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

Caution: fundamental data quality issues underlying intelligent prediction of enzyme optimal pH. 注意：智能预测酶的最佳pH值的基础数据质量问题。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag543

Peng Zhou, Xuzhong Fang

引用次数: 0

Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics. 单细胞多组学中弱监督细胞类型标注的拓扑引导双掩膜对比学习。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag546

Yi Zhang, Longneng Ran, Yu Liang, Xiaohua Wang, Hanyan Wei

{"title":"Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.","authors":"Yi Zhang, Longneng Ran, Yu Liang, Xiaohua Wang, Hanyan Wei","doi":"10.1093/bib/bbag546","DOIUrl":"10.1093/bib/bbag546","url":null,"abstract":"Single-cell multi-omics technologies provide complementary views of cellular states, but are challenged by pervasive feature sparsity, pronounced cross-modality heterogeneity, and a critical shortage of labeled cells, hindering accurate cell-type annotation. We propose dual-masked contrastive learning (DMCL), a topology-guided weakly supervised cross-modal representation learning framework that addresses these challenges within a unified architecture. DMCL first constructs modality-specific K-nearest neighbor graphs to capture cell-cell topological relationships within each omics layer. A dual masking strategy combining global column-wise masking and local random masking is then applied to simulate realistic feature missingness and regularize the model against technical noise. Topology-aware latent representations are subsequently derived through a graph autoencoder that faithfully preserves the local cellular neighborhood structure of each modality. Cross-modal integration is achieved via a multi-head self-attention module that dynamically weights modality-specific features according to per-cell informativeness, enabling context-adaptive modality fusion. To further improve representation quality, DMCL incorporates a dual contrastive objective that jointly enforces intra-cell view consistency and inter-cell neighborhood structure, thereby enhancing both cross-modality alignment and structural preservation in the latent space. Extensive experiments on diverse real and simulated datasets demonstrate that DMCL achieves accurate cell-type annotation and superior clustering performance under minimal label supervision, establishing a principled and scalable foundation for weakly supervised single-cell multimodal analysis.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135225","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

Unsupervised identification of low-frequency antigen-specific TCRs using distance-based anomaly scoring. 使用基于距离的异常评分的低频抗原特异性tcr的无监督识别。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag511

Kyohei Kinoshita, Tetsuya J Kobayashi

{"title":"Unsupervised identification of low-frequency antigen-specific TCRs using distance-based anomaly scoring.","authors":"Kyohei Kinoshita, Tetsuya J Kobayashi","doi":"10.1093/bib/bbag511","DOIUrl":"10.1093/bib/bbag511","url":null,"abstract":"Identifying antigen-specific T cell receptors (TCRs) within the diverse human repertoire remains challenging, particularly for low-frequency clonotypes. Here, we present TCR-RADAR (TCR Rare Antigen-specific Detection by Anomaly Ranking), an unsupervised approach that detects low-frequency antigen-specific TCRs through distance-based anomaly detection in TCR sequence space. Using TCRdist3 to quantify sequence distances, we identify query TCRs that are anomalous relative to reference repertoires within their V-J gene combinations. We validated this approach across three immunological contexts: COVID-19 infection, influenza vaccination, and yellow fever vaccination. For SARS-CoV-2-specific TCR detection in a COVID-19 patient, our method achieved 34.3% precision, substantially higher than similarity-based (ALICE: 8.0%) and frequency-based methods (edgeR: 5.8%, the Pogorelyy method: 6.3%), and uniquely detected low-frequency antigen-specific TCRs present at a clone count of 1. The minimal overlap with conventional approaches (0%-6.7% across the three datasets) indicates our method captures distinct TCR clones overlooked by existing analyses. This distance-based approach provides a complementary strategy for TCR specificity detection, particularly valuable for identifying rare antigen-specific clones essential for understanding immune responses.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135205","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

VeloHARMO: a deep learning framework for mRNA coding sequence generation and design via translation velocity harmonization. VeloHARMO：一个通过翻译速度协调生成和设计mRNA编码序列的深度学习框架。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-09-01 DOI: 10.1093/bib/bbag558

Bian Bian, Jichen Zhang, Yiming Zhang, Yutaka Saito

{"title":"VeloHARMO: a deep learning framework for mRNA coding sequence generation and design via translation velocity harmonization.","authors":"Bian Bian, Jichen Zhang, Yiming Zhang, Yutaka Saito","doi":"10.1093/bib/bbag558","DOIUrl":"10.1093/bib/bbag558","url":null,"abstract":"Heterologous gene expression often results in suboptimal protein yield or activity, potentially influenced by differences in translation dynamics between native and host organisms. Conventional mRNA design approaches based on codon usage frequency can improve expression efficiency. However, codon usage frequency alone does not necessarily reflect codon-level translation velocity. Here, we present VeloHARMO, a computational framework for mRNA coding sequence design based on translation velocity harmonization. VeloHARMO combines organism-specific deep learning models trained on Ribo-seq data with a genetic algorithm to generate synonymous coding sequences that preserve native translation velocity patterns across organisms. Computational evaluations show that VeloHARMO achieves higher prediction accuracy and more effective translation velocity harmonization than existing approaches. Furthermore, analyses of previously reported synonymous mRNA variants suggest that translation velocity harmonization may help explain differences observed among alternative coding sequences. Collectively, VeloHARMO introduces translation velocity harmonization as a data-driven principle for computational mRNA design and provides a framework for incorporating organism-specific translation dynamics into coding sequence engineering.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135231","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