Bioinformatics advances最新文献

MultiOmicsIntegrator: a nextflow pipeline for integrated omics analyses. MultiOmicsIntegrator：一个用于综合 omics 分析的 nextflow 管道。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-14 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae175

Bianka Alexandra Pasat, Eleftherios Pilalis, Katarzyna Mnich, Afshin Samali, Aristotelis Chatziioannou, Adrienne M Gorman

{"title":"MultiOmicsIntegrator: a nextflow pipeline for integrated omics analyses.","authors":"Bianka Alexandra Pasat, Eleftherios Pilalis, Katarzyna Mnich, Afshin Samali, Aristotelis Chatziioannou, Adrienne M Gorman","doi":"10.1093/bioadv/vbae175","DOIUrl":"https://doi.org/10.1093/bioadv/vbae175","url":null,"abstract":"Motivation: Analysis of gene and isoform expression levels is becoming critical for the detailed understanding of biochemical mechanisms. In addition, integrating RNA-seq data with other omics data types, such as proteomics and metabolomics, provides a strong approach for consolidating our understanding of biological processes across various organizational tiers, thus promoting the identification of potential therapeutic targets.Results: We present our pipeline, called MultiOmicsIntegrator (MOI), an inclusive pipeline for comprehensive omics analyses. MOI represents a unified approach that performs in-depth individual analyses of diverse omics. Specifically, exhaustive analysis of RNA-seq data at the level of genes, isoforms of genes, as well as miRNA is offered, coupled with functional annotation and structure prediction of these transcripts. Additionally, proteomics and metabolomics data are supported providing a holistic view of biological systems. Finally, MOI has tools to integrate simultaneously multiple and diverse omics datasets, with both data- and function-driven approaches, fostering a deeper understanding of intricate biological interactions.Availability and implementation: MOI and ReadTheDocs.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae175"},"PeriodicalIF":2.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683388","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

mxfda: a comprehensive toolkit for functional data analysis of single-cell spatial data. mxfda：用于单细胞空间数据功能数据分析的综合工具包。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-13 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae155

Julia Wrobel, Alex C Soupir, Mitchell T Hayes, Lauren C Peres, Thao Vu, Andrew Leroux, Brooke L Fridley

引用次数: 0

Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics. 以系统发育为基础的图深度学习对传染病流行中的动态传播集群进行分类。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-07 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae158

Chaoyue Sun, Yanjun Li, Simone Marini, Alberto Riva, Dapeng Oliver Wu, Ruogu Fang, Marco Salemi, Brittany Rife Magalis

{"title":"Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics.","authors":"Chaoyue Sun, Yanjun Li, Simone Marini, Alberto Riva, Dapeng Oliver Wu, Ruogu Fang, Marco Salemi, Brittany Rife Magalis","doi":"10.1093/bioadv/vbae158","DOIUrl":"https://doi.org/10.1093/bioadv/vbae158","url":null,"abstract":"Motivation: In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g. infectivity) or host (e.g. vaccination), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population.Results: We evaluated the limitation of existing tree shape metrics when dealing with dynamic transmission clusters and propose instead a phylogeny-based deep learning system -DeepDynaTree- for dynamic classification. Comprehensive experiments carried out on a variety of simulated epidemic growth models and HIV epidemic data indicate that this graph deep learning approach is effective, robust, and informative for cluster dynamic prediction. Our results confirm that DeepDynaTree is a promising tool for transmission cluster characterization that can be modified to address the existing limitations and deficiencies in knowledge regarding the dynamics of transmission trajectories for groups at risk of pathogen infection.Availability and implementation: DeepDynaTree is available under an MIT Licence in https://github.com/salemilab/DeepDynaTree.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae158"},"PeriodicalIF":2.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633757","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

AAclust: k-optimized clustering for selecting redundancy-reduced sets of amino acid scales. AAclust：用于选择减少冗余的氨基酸尺度集的 k 优化聚类。

IF 2.4

Bioinformatics advances Pub Date : 2024-10-30 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae165

Stephan Breimann, Dmitrij Frishman

{"title":"AAclust: k-optimized clustering for selecting redundancy-reduced sets of amino acid scales.","authors":"Stephan Breimann, Dmitrij Frishman","doi":"10.1093/bioadv/vbae165","DOIUrl":"10.1093/bioadv/vbae165","url":null,"abstract":"Summary: Amino acid scales are crucial for sequence-based protein prediction tasks, yet no gold standard scale set or simple scale selection methods exist. We developed AAclust, a wrapper for clustering models that require a pre-defined number of clusters k, such as k-means. AAclust obtains redundancy-reduced scale sets by clustering and selecting one representative scale per cluster, where k can either be optimized by AAclust or defined by the user. The utility of AAclust scale selections was assessed by applying machine learning models to 24 protein benchmark datasets. We found that top-performing scale sets were different for each benchmark dataset and significantly outperformed scale sets used in previous studies. Noteworthy is the strong dependence of the model performance on the scale set size. AAclust enables a systematic optimization of scale-based feature engineering in machine learning applications.Availability and implementation: The AAclust algorithm is part of AAanalysis, a Python-based framework for interpretable sequence-based protein prediction, which is documented and accessible at https://aaanalysis.readthedocs.io/en/latest and https://github.com/breimanntools/aaanalysis.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae165"},"PeriodicalIF":2.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636210","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

Exon nomenclature and classification of transcripts database (ENACTdb): a resource for analyzing alternative splicing mediated proteome diversity. 外显子命名和转录本分类数据库（ENACTdb）：分析替代剪接介导的蛋白质组多样性的资源。

IF 2.4

Bioinformatics advances Pub Date : 2024-10-29 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae157

Paras Verma, Deeksha Thakur, Shashi B Pandit

{"title":"Exon nomenclature and classification of transcripts database (ENACTdb): a resource for analyzing alternative splicing mediated proteome diversity.","authors":"Paras Verma, Deeksha Thakur, Shashi B Pandit","doi":"10.1093/bioadv/vbae157","DOIUrl":"https://doi.org/10.1093/bioadv/vbae157","url":null,"abstract":"Motivation: Gene transcripts are distinguished by the composition of their exons, and this different exon composition may contribute to advancing proteome complexity. Despite the availability of alternative splicing information documented in various databases, a ready association of exonic variations to the protein sequence remains a mammoth task.Results: To associate exonic variation(s) with the protein systematically, we designed the Exon Nomenclature and Classification of Transcripts (ENACT) framework for uniquely annotating exons that tracks their loci in gene architecture context with encapsulating variations in splice site(s) and amino acid coding status. After ENACT annotation, predicted protein features (secondary structure/disorder/Pfam domains) are mapped to exon attributes. Thus, ENACTdb provides trackable exonic variation(s) association to isoform(s) and protein features, enabling the assessment of functional variation due to changes in exon composition. Such analyses can be readily performed through multiple views supported by the server. The exon-centric visualizations of ENACT annotated isoforms could provide insights on the functional repertoire of genes due to alternative splicing and its related processes and can serve as an important resource for the research community.Availability and implementation: The database is publicly available at https://www.iscbglab.in/enactdb/. It contains protein-coding genes and isoforms for Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus, and Homo sapiens.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae157"},"PeriodicalIF":2.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683380","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

MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields. MicroNet-MIMRF：基于互信息和马尔可夫随机场的微生物网络推断方法。

IF 2.4

Bioinformatics advances Pub Date : 2024-10-28 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae167

Chenqionglu Feng, Huiqun Jia, Hui Wang, Jiaojiao Wang, Mengxuan Lin, Xiaoyan Hu, Chenjing Yu, Hongbin Song, Ligui Wang

{"title":"MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields.","authors":"Chenqionglu Feng, Huiqun Jia, Hui Wang, Jiaojiao Wang, Mengxuan Lin, Xiaoyan Hu, Chenjing Yu, Hongbin Song, Ligui Wang","doi":"10.1093/bioadv/vbae167","DOIUrl":"https://doi.org/10.1093/bioadv/vbae167","url":null,"abstract":"Motivation: The human microbiome, comprises complex associations and communication networks among microbial communities, which are crucial for maintaining health. The construction of microbial networks is vital for elucidating these associations. However, existing microbial networks inference methods cannot solve the issues of zero-inflation and non-linear associations. Therefore, necessitating novel methods to improve the accuracy of microbial networks inference.Results: In this study, we introduce the Microbial Network based on Mutual Information and Markov Random Fields (MicroNet-MIMRF) as a novel approach for inferring microbial networks. Abundance data of microbes are modeled through the zero-inflated Poisson distribution, and the discrete matrix is estimated for further calculation. Markov random fields based on mutual information are used to construct accurate microbial networks. MicroNet-MIMRF excels at estimating pairwise associations between microbes, effectively addressing zero-inflation and non-linear associations in microbial abundance data. It outperforms commonly used techniques in simulation experiments, achieving area under the curve values exceeding 0.75 for all parameters. A case study on inflammatory bowel disease data further demonstrates the method's ability to identify insightful associations. Conclusively, MicroNet-MIMRF is a powerful tool for microbial network inference that handles the biases caused by zero-inflation and overestimation of associations.Availability and implementation: The MicroNet-MIMRF is provided at https://github.com/Fionabiostats/MicroNet-MIMRF.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae167"},"PeriodicalIF":2.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11549015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633755","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

MetaboScope: a statistical toolbox for analyzing ¹H nuclear magnetic resonance spectra from human clinical studies. MetaboScope：用于分析人体临床研究 1H 核磁共振谱的统计工具箱。

IF 2.4

Bioinformatics advances Pub Date : 2024-10-28 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae142

Ruey Leng Loo, Javier Osorio Mosquera, Michael Zasso, Jacqueline Mathews, Desmond G Johnston, Jeremy K Nicholson, Luc Patiny, Elaine Holmes, Julien Wist

{"title":"MetaboScope: a statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies.","authors":"Ruey Leng Loo, Javier Osorio Mosquera, Michael Zasso, Jacqueline Mathews, Desmond G Johnston, Jeremy K Nicholson, Luc Patiny, Elaine Holmes, Julien Wist","doi":"10.1093/bioadv/vbae142","DOIUrl":"https://doi.org/10.1093/bioadv/vbae142","url":null,"abstract":"Motivation: Metabolic phenotyping, using high-resolution spectroscopic molecular fingerprints of biological samples, has demonstrated diagnostic, prognostic, and mechanistic value in clinical studies. However, clinical translation is hindered by the lack of viable workflows and challenges in converting spectral data into usable information.Results: MetaboScope is an analytical and statistical workflow for learning, designing and analyzing clinically relevant 1H nuclear magnetic resonance data. It features modular preprocessing pipelines, multivariate modeling tools including Principal Components Analysis (PCA), Orthogonal-Projection to Latent Structure Discriminant Analysis (OPLS-DA), and biomarker discovery tools (multiblock PCA and statistical spectroscopy). A simulation tool is also provided, allowing users to create synthetic spectra for hypothesis testing and power calculations.Availability and implementation: MetaboScope is built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The MetaboScope tool is available at https://www.cheminfo.org/flavor/metabolomics/index.html. The code is open-source and can be deployed locally if necessary. Module notes, video tutorials, and clinical spectral datasets are provided for modeling.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae142"},"PeriodicalIF":2.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683385","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

motifbreakR v2: expanded variant analysis including indels and integrated evidence from transcription factor binding databases. motifbreakR v2：扩展的变异分析，包括嵌合和来自转录因子结合数据库的综合证据。

IF 2.4

Bioinformatics advances Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae162

Simon G Coetzee, Dennis J Hazelett

{"title":"motifbreakR v2: expanded variant analysis including indels and integrated evidence from transcription factor binding databases.","authors":"Simon G Coetzee, Dennis J Hazelett","doi":"10.1093/bioadv/vbae162","DOIUrl":"https://doi.org/10.1093/bioadv/vbae162","url":null,"abstract":"Motivation: motifbreakR scans genetic variants against position weight matrices of transcription factors (TFs) to determine the potential for the disruption of binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to query a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single-nucleotide variants on TF binding sites, in motifbreakR v2, we have updated the functionality.Results: New features include the ability to query other types of complex genetic variants, such as short insertions and deletions. This capability allows modeling a more extensive array of variants that may have significant effects on TF binding. Additionally, predictions based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR can directly query the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.Availability and implementation: motifbreakR is implemented in R. Source code, documentation, and tutorials are available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and GitHub at https://github.com/Simon-Coetzee/motifBreakR.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae162"},"PeriodicalIF":2.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549260","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

TransAnnot-a fast transcriptome annotation pipeline. TransAnnot--快速转录组注释管道。

IF 2.4

Bioinformatics advances Pub Date : 2024-10-22 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae152

Mariia Zelenskaia, Yazhini Arangasamy, Milot Mirdita, Johannes Söding, Venket Raghavan

{"title":"TransAnnot-a fast transcriptome annotation pipeline.","authors":"Mariia Zelenskaia, Yazhini Arangasamy, Milot Mirdita, Johannes Söding, Venket Raghavan","doi":"10.1093/bioadv/vbae152","DOIUrl":"10.1093/bioadv/vbae152","url":null,"abstract":"Summary: The annotation of deeply sequenced, de novo assembled transcriptomes continues to be a challenge as some of the state-of-the-art tools are slow, difficult to install, and hard to use. We have tackled these issues with TransAnnot, a fast, automated transcriptome annotation pipeline that is easy to install and use. Leveraging the fast sequence searches provided by the MMseqs2 suite, TransAnnot offers one-step annotation of homologs from Swiss-Prot, gene ontology terms and orthogroups from eggNOG, and functional domains from Pfam. Users also have the option to annotate against custom databases. TransAnnot accepts sequencing reads (short and long), nucleotide sequences, or amino acid sequences as input for annotation. When benchmarked with test data sets of amino acid sequences, TransAnnot was 333, 284, and 18 times faster than comparable tools such as EnTAP, Trinotate, and eggNOG-mapper respectively.Availability and implementation: TransAnnot is free to use, open sourced under GPLv3, and is implemented in C++ and Bash. Source code, documentation, and pre-compiled binaries are available at https://github.com/soedinglab/transannot. TransAnnot is also available via bioconda (https://anaconda.org/bioconda/transannot).","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae152"},"PeriodicalIF":2.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570211","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

ProCogGraph: a graph-based mapping of cognate ligand domain interactions. ProCogGraph：基于图形的同源配体结构域相互作用图谱。

IF 2.4

Bioinformatics advances Pub Date : 2024-10-22 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae161

Matthew Crown, Matthew Bashton

{"title":"ProCogGraph: a graph-based mapping of cognate ligand domain interactions.","authors":"Matthew Crown, Matthew Bashton","doi":"10.1093/bioadv/vbae161","DOIUrl":"10.1093/bioadv/vbae161","url":null,"abstract":"Motivation: Mappings of domain-cognate ligand interactions can enhance our understanding of the core concepts of evolution and be used to aid docking and protein design. Since the last available cognate-ligand domain database was released, the PDB has grown significantly and new tools are available for measuring similarity and determining contacts.Results: We present ProCogGraph, a graph database of cognate-ligand domain mappings in PDB structures. Building upon the work of the predecessor database, PROCOGNATE, we use data-driven approaches to develop thresholds and interaction modes. We explore new aspects of domain-cognate ligand interactions, including the chemical similarity of bound cognate ligands and how domain combinations influence cognate ligand binding. Finally, we use the graph to add specificity to partial EC IDs, showing that ProCogGraph can complete partial annotations systematically through assigned cognate ligands.Availability and implementation: The ProCogGraph pipeline, database and flat files are available at https://github.com/bashton-lab/ProCogGraph and https://doi.org/10.5281/zenodo.13165851.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae161"},"PeriodicalIF":2.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633761","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