Bioinformatics advances最新文献_第3页

DgeaHeatmap: an R package for transcriptomic analysis and heatmap generation. geaheatmap：一个R包转录组分析和热图生成。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-20 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf194

Leonie J Lancelle, Phani S Potru, Björn Spittau, Susanne Wiemann

{"title":"DgeaHeatmap: an R package for transcriptomic analysis and heatmap generation.","authors":"Leonie J Lancelle, Phani S Potru, Björn Spittau, Susanne Wiemann","doi":"10.1093/bioadv/vbaf194","DOIUrl":"10.1093/bioadv/vbaf194","url":null,"abstract":"Motivation: The growing use of transcriptomic data from platforms like Nanostring GeoMx DSP demands accessible and flexible tools for differential gene expression analysis and heatmap generation. Current web-based tools often lack transparency, modifiability, and independence from external servers creating barriers for researchers seeking customizable workflows, as well as data privacy and security. Additionally, tools that can be utilized by individuals with minimal bioinformatics expertise provide an inclusive solution, empowering a broader range of users to analyze complex data effectively.Results: Here, we introduce Differential Gene Expression Analysis and Heatmaps (DgeaHeatmap), an R package offering streamlined and user-friendly functions for transcriptomic data analysis particularly yielded by Nanostring GeoMx DSP instruments. The package supports both normalized and raw count data, providing tools to preprocess, filter, and annotate datasets. DgeaHeatmap leverages Z-score scaling and k-means clustering for customizable heatmap generation and incorporates a workflow adapted from GeoMxTools for handling raw Nanostring GeoMx DSP data. By enabling server-independent analyses, the package enhances flexibility, transparency, and reproducibility in transcriptomic research.Availability and implementation: The package DgeaHeatmap is freely available on GitLab (https://gitlab.ub.uni-bielefeld.de/spittaulab/Dgea_Heatmap_Package.git).","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf194"},"PeriodicalIF":2.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12401572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal deep learning for predicting protein ubiquitination sites. 多模态深度学习预测蛋白质泛素化位点。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-20 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf200

Subash C Pakhrin, Moriah R Beck, Punjan Subedi, Rabina Lama, Simonsha Shrestha

{"title":"Multimodal deep learning for predicting protein ubiquitination sites.","authors":"Subash C Pakhrin, Moriah R Beck, Punjan Subedi, Rabina Lama, Simonsha Shrestha","doi":"10.1093/bioadv/vbaf200","DOIUrl":"10.1093/bioadv/vbaf200","url":null,"abstract":"Motivation: Ubiquitination is a crucial post-translational modification that regulates various biological functions, including protein degradation, signal transduction, and cellular homeostasis. Accurate identification of ubiquitination sites is essential for understanding these mechanisms, yet existing prediction tools often lack generalizability across diverse datasets. To address this limitation, we developed Multimodal Ubiquitination Predictor, a deep learning-based approach capable of predicting ubiquitination sites across general, human-specific, and plant-specific datasets. By integrating diverse protein sequence representations-one-hot encoding, embeddings, and physicochemical properties-within a unified deep-learning framework, the proposed method significantly enhances prediction accuracy and robustness, offering a valuable resource for both research and applications in ubiquitination site discovery.Results: Multimodal Ubiquitination Predictor achieved superior performance across general, human-specific, and plant-specific datasets, with 77.25% accuracy, 74.98% sensitivity, 80.67% specificity, an MCC of 0.54, and an AUC of 0.87 on an independent human ubiquitination test dataset. It outperformed existing methods, demonstrating enhanced reliability for ubiquitination site prediction. This robust predictor and dataset serve as valuable resources for future research and discovery.Availability and implementation: The developed tool, programs, training, and test dataset are available at https://github.com/PakhrinLab/MMUbiPred.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf200"},"PeriodicalIF":2.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12408473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MBC PathNet: integration and visualization of networks connecting functionally related pathways predicted from transcriptomic and proteomic datasets. MBC PathNet：整合和可视化从转录组学和蛋白质组学数据集预测的连接功能相关通路的网络。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-18 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf197

Jens Hansen, Ravi Iyengar

{"title":"MBC PathNet: integration and visualization of networks connecting functionally related pathways predicted from transcriptomic and proteomic datasets.","authors":"Jens Hansen, Ravi Iyengar","doi":"10.1093/bioadv/vbaf197","DOIUrl":"10.1093/bioadv/vbaf197","url":null,"abstract":"Motivation: Advances in high-throughput technologies have shifted the focus from bulk to single cell or spatial transcriptomic and proteomic analysis of tissues and cell cultures. The resulting increase in gene and/or protein lists leads to the subsequent growth of up- and downregulated pathways lists. This trend creates the need for pathway-network based integration strategies that allow quick exploration of shared and distinct mechanisms across datasets.Results: Here, we present Molecular Biology of the Cell (MBC) Pathway Networks (PathNet). MBC PathNet allows for quick and easy integration and visualization of networks of functionally related pathways predicted from gene and protein lists using the Molecular Biology of the Cell Ontology and other ontologies. Within networks of hierarchical parent-child relationships or functional relationships, pathways are visualized as pie charts where each slice represents a dataset that predicted that pathway. Sizes of pies and slices can be selected to represent statistical significance or other quantitative measures. In addition, MBC PathNet can generate bar diagrams, heatmaps, and timelines. Fully automated execution from the command line is supported.Availability and implementation: iyengarlab.org/mbcpathnet; mbc-ontology.org; github.com/SBCNY/Molecular-Biology-of-the-Cell.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf197"},"PeriodicalIF":2.8,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 foundation model for learning genetic associations from brain imaging phenotypes. 从脑成像表型中学习遗传关联的基础模型。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-13 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf196

Diego Machado Reyes, Myson Burch, Laxmi Parida, Aritra Bose

{"title":"A foundation model for learning genetic associations from brain imaging phenotypes.","authors":"Diego Machado Reyes, Myson Burch, Laxmi Parida, Aritra Bose","doi":"10.1093/bioadv/vbaf196","DOIUrl":"10.1093/bioadv/vbaf196","url":null,"abstract":"Motivation: Due to the intricate etiology of neurological disorders, finding interpretable associations between multiomics features can be challenging using standard approaches.Results: We propose COMICAL, a contrastive learning approach using multiomics data to generate associations between genetic markers and brain imaging-derived phenotypes. COMICAL jointly learns omics representations utilizing transformer-based encoders with custom tokenizers. Our modality-agnostic approach uniquely identifies many-to-many associations via self-supervised learning schemes and cross-modal attention encoders. COMICAL discovered several significant associations between genetic markers and imaging-derived phenotypes for a variety of neurological disorders in the UK Biobank, as well as prediction of diseases and unseen clinical outcomes from learned representations.Availability and implementation: The source code of COMICAL along with pretrained weights, enabling transfer learning, is available at https://github.com/IBM/comical.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf196"},"PeriodicalIF":2.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12410928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information. 协调异质单细胞基因表达数据与个体水平协变量信息。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-09 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf189

Yudi Mu, Wei Vivian Li

{"title":"Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information.","authors":"Yudi Mu, Wei Vivian Li","doi":"10.1093/bioadv/vbaf189","DOIUrl":"10.1093/bioadv/vbaf189","url":null,"abstract":"Motivation: The growing availability of single-cell RNA sequencing (scRNA-seq) data highlights the necessity for robust integration methods to uncover both shared and unique cellular features across samples. These datasets often exhibit technical variations and biological differences, complicating integrative analyses. While numerous integration methods have been proposed, many fail to account for individual-level covariates or are limited to discrete variables.Results: To address these limitations, we propose scINSIGHT2, a generalized linear latent variable model that accommodates both continuous covariates, such as age, and discrete factors, such as disease conditions. Through both simulation studies and real-data applications, we demonstrate that scINSIGHT2 accurately harmonizes scRNA-seq datasets, whether from single or multiple sources. These results highlight scINSIGHT2's utility in capturing meaningful biological insights from scRNA-seq data while accounting for individual-level variation.Availability and implementation: The scINSIGHT2 method has been implemented as a R package, which is available at https://github.com/yudimu/scINSIGHT2/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf189"},"PeriodicalIF":2.8,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12380451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Next generation biobanking ontology: introducing-omics contextual data to biobanking ontology. 下一代生物银行本体：将组学上下文数据引入生物银行本体。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-07 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf131

Dalia Alghamdi, Damion M Dooley, Mannar Samman, Ali AlFaiz, William W L Hsiao

{"title":"Next generation biobanking ontology: introducing-omics contextual data to biobanking ontology.","authors":"Dalia Alghamdi, Damion M Dooley, Mannar Samman, Ali AlFaiz, William W L Hsiao","doi":"10.1093/bioadv/vbaf131","DOIUrl":"10.1093/bioadv/vbaf131","url":null,"abstract":"Motivation: With improvements in high throughput sequencing technologies and the constant generation of large biomedical datasets, biobanks increasingly take on the role of managing and delivering not just specimens but also specimen-derived data and associated contextual data. However, reusing data from different biobanks is challenged by incompatible data representations. Contextual data describing biobank resources often contains unstructured textual information incompatible with computational processes such as automated data discovery and integration. Therefore, a consistent and comprehensive contextual data framework is needed to increase discovery, reusability, and integrability across data sources.Results: The next generation biobanking ontology is an open-source application ontology representing omics contextual data, licensed under the Creative Commons 4.0 license. The ontology focuses on capturing information about three main activities: wet bench analysis used to generate omics data, bioinformatics analysis used to process and interpret data, and data management. In this paper, we demonstrated the use of the ontology to add semantic statements to real-life use cases and query data previously stored in unstructured textual format.Availability and implementation: NGBO is freely available at https://github.com/Dalalghamdi/NGBO, and accessible from OLS https://www.ebi.ac.uk/ols4/ontologies/ngbo.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf131"},"PeriodicalIF":2.8,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

robin2: accelerating single-cell data clustering evaluation. Robin2：加速单细胞数据聚类评估。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-06 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf184

Valeria Policastro, Dario Righelli, Luisa Cutillo, Annamaria Carissimo

{"title":"robin2: accelerating single-cell data clustering evaluation.","authors":"Valeria Policastro, Dario Righelli, Luisa Cutillo, Annamaria Carissimo","doi":"10.1093/bioadv/vbaf184","DOIUrl":"10.1093/bioadv/vbaf184","url":null,"abstract":"Motivation: The rapid expansion of single-cell RNA sequencing (scRNA-seq) technologies has increased the need for robust and scalable clustering evaluation methods. To address these challenges, we developed robin2, an optimized version of our R package robin. It introduces enhanced computational efficiency, support for high-dimensional datasets, and harmonious integration with R's base functionalities for robust network analysis.Results: robin2 offers improved functionality for clustering stability validation and enables systematic evaluation of community detection algorithms across various resolutions and pipelines. The application to Tabula Muris and PBMC scRNA-seq datasets confirmed its ability to identify biologically meaningful cell subpopulations with high statistical significance. The new version reduces computational time by 9-fold on large-scale datasets using parallel processing.Availability and implementation: The robin2 package is freely available on CRAN at https://CRAN.R-project.org/package=robin. Comprehensive documentation and a detailed analysis vignette are available on GitHub at https://drighelli.github.io/scrobinv2/index.html.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf184"},"PeriodicalIF":2.8,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous graph neural networks for link prediction in biomedical networks. 异构图神经网络在生物医学网络中的链接预测。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-05 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf187

Junwei Hu, Michael Bewong, Selasi Kwashie, Wen Zhang, Hong-Yu Zhang, Zaiwen Feng

{"title":"Heterogeneous graph neural networks for link prediction in biomedical networks.","authors":"Junwei Hu, Michael Bewong, Selasi Kwashie, Wen Zhang, Hong-Yu Zhang, Zaiwen Feng","doi":"10.1093/bioadv/vbaf187","DOIUrl":"10.1093/bioadv/vbaf187","url":null,"abstract":"Summary: Heterogeneous graph neural networks (HGNNs) are gaining popularity as powerful tools for analysing complex networks with diverse node types often referred to as heterogeneous graphs. While existing HGNNs have been successfully used within the context of social and information networks, their application in biomedicine remains limited. In this study, we posit the utility of readily available generic HGNNs in addressing the link prediction tasks in biomedical settings. Thus, we conduct a benchmarking study of 42 techniques including nine generic HGNNs across eight biomedical datasets using several evaluation metrics. Our results show that the recently developed and readily available generic HGNNs achieve comparable and sometimes better results when compared with the specialized biomedical methods across all evaluation metrics. For instance, the generic HGNN Simple-HGN achieves the best results in four of the eight datasets and shows equivalent performance to the biomedical methods on the remaining datasets. Furthermore, this work analyses and presents useful guidelines to practitioners on how to optimally set complex hyperparameters which underpin the HGNNs.Availability and implementation: Finally, this work makes publicly available, via https://github.com/Zaiwen/Link_Prediction_in_Biomedical_Network, the benchmarking framework and source codes which underpin this study.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf187"},"PeriodicalIF":2.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Marker genes reveal dynamic features of cell evolving processes. 标记基因揭示细胞进化过程的动态特征。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-05 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf185

Wenjie Cao, Bengong Zhang, Tianshou Zhou

{"title":"Marker genes reveal dynamic features of cell evolving processes.","authors":"Wenjie Cao, Bengong Zhang, Tianshou Zhou","doi":"10.1093/bioadv/vbaf185","DOIUrl":"10.1093/bioadv/vbaf185","url":null,"abstract":"Motivation: Embryonic cells finally evolve into various types of mature cells, where cell fate determinations play pivotal roles, but dynamic features of this process remain elusive.Results: We analyze four single-cell RNA sequencing datasets on mouse embryo cells, mouse embryonic fibroblasts, human bone marrow, and intestine organoid. We show that key (high expression) genes of each organism exhibit different statistical features and expression patterns before and after branch, e.g. for mouse embryo cells, the mRNA distribution of gene Gata3 is bimodal before branch, unimodal at branching point and trimodal for one branch but bimodal for the other branch. Moreover, there is a distribution mode such that it is the same before and after branch, and this fact would account for maintenance of the genetic information in a complex cell evolving process. Machine learning reveal that along the cell pseudo-time trajectory, the strength that one key gene regulates another is fundamentally increasing before branch but is always monotonically increasing after branch; burst size and frequency of key genes are always monotonically decreasing before branch but monotonically increasing for one branch and monotonically decreasing for another branch. Our results unveil the essential features of dynamic cell processes and can be taken as a supplement for accurately screening marker genes of cell fate determination on basis of the existed methods.Availability and implementation: The implementation of CFD is available at https://github.com/cellwj/CFD and the preprocessed data is available at https://zenodo.org/records/14367638.Cell fate determination, single-cell RNA sequencing data, marker gene, cell process, developmental branch.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf185"},"PeriodicalIF":2.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Struc2mapGAN: improving synthetic cryogenic electron microscopy density maps with generative adversarial networks. Struc2mapGAN：利用生成对抗网络改进合成低温电子显微镜密度图。

IF 2.8

Bioinformatics advances Pub Date : 2025-08-04 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf179

Chenwei Zhang, Anne Condon, Khanh Dao Duc

{"title":"Struc2mapGAN: improving synthetic cryogenic electron microscopy density maps with generative adversarial networks.","authors":"Chenwei Zhang, Anne Condon, Khanh Dao Duc","doi":"10.1093/bioadv/vbaf179","DOIUrl":"10.1093/bioadv/vbaf179","url":null,"abstract":"Motivation: Generating synthetic cryogenic electron microscopy 3D density maps from molecular structures has potential important applications in structural biology. Yet existing simulation-based methods cannot mimic all the complex features present in experimental maps, such as secondary structure elements. As an alternative, we propose struc2mapGAN.Results: Struc2mapGAN is a novel data-driven method that employs a generative adversarial network to produce improved experimental-like density maps from molecular structures. More specifically, struc2mapGAN uses a nested U-Net architecture as the generator, with an additional L1 loss term and further processing of raw training experimental maps to enhance learning efficiency. While struc2mapGAN can promptly generate maps after training, we demonstrate that it outperforms existing simulation-based methods for a wide array of tested maps and across various evaluation metrics.Availability and implementation: The struc2mapGAN is publicly accessible via https://github.com/chenwei-zhang/struc2mapGAN.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf179"},"PeriodicalIF":2.8,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0