Bioinformatics最新文献_第7页

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs. BioThings Explorer：用于生物医学API的联合知识图的查询引擎。

IF 4.4 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad570

Jackson Callaghan, Colleen H Xu, Jiwen Xin, Marco Alvarado Cano, Anders Riutta, Eric Zhou, Rohan Juneja, Yao Yao, Madhumita Narayan, Kristina Hanspers, Ayushi Agrawal, Alexander R Pico, Chunlei Wu, Andrew I Su

{"title":"BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.","authors":"Jackson Callaghan, Colleen H Xu, Jiwen Xin, Marco Alvarado Cano, Anders Riutta, Eric Zhou, Rohan Juneja, Yao Yao, Madhumita Narayan, Kristina Hanspers, Ayushi Agrawal, Alexander R Pico, Chunlei Wu, Andrew I Su","doi":"10.1093/bioinformatics/btad570","DOIUrl":"10.1093/bioinformatics/btad570","url":null,"abstract":"Summary: Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time.Availability and implementation: More information can be found at https://explorer.biothings.io and code is available at https://github.com/biothings/biothings_explorer.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11015316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10287315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

MuTATE-an R package for comprehensive multi-objective molecular modeling. mutate -一个用于综合多目标分子建模的R包。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad507

Sarah G Ayton, Víctor Treviño

{"title":"MuTATE-an R package for comprehensive multi-objective molecular modeling.","authors":"Sarah G Ayton, Víctor Treviño","doi":"10.1093/bioinformatics/btad507","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad507","url":null,"abstract":"Motivation: Comprehensive multi-omics studies have driven advances in disease modeling for effective precision medicine but pose a challenge for existing machine-learning approaches, which have limited interpretability across clinical endpoints. Automated, comprehensive disease modeling requires a machine-learning approach that can simultaneously identify disease subgroups and their defining molecular biomarkers by explaining multiple clinical endpoints. Current tools are restricted to individual endpoints or limited variable types, necessitate advanced computation skills, and require resource-intensive manual expert interpretation.Results: We developed Multi-Target Automated Tree Engine (MuTATE) for automated and comprehensive molecular modeling, which enables user-friendly multi-objective decision tree construction and visualization of relationships between molecular biomarkers and patient subgroups characterized by multiple clinical endpoints. MuTATE incorporates multiple targets throughout model construction and allows for target weights, enabling construction of interpretable decision trees that provide insights into disease heterogeneity and molecular signatures. MuTATE eliminates the need for manual synthesis of multiple non-explainable models, making it highly efficient and accessible for bioinformaticians and clinicians. The flexibility and versatility of MuTATE make it applicable to a wide range of complex diseases, including cancer, where it can improve therapeutic decisions by providing comprehensive molecular insights for precision medicine. MuTATE has the potential to transform biomarker discovery and subtype identification, leading to more effective and personalized treatment strategies in precision medicine, and advancing our understanding of disease mechanisms at the molecular level.Availability and implementation: MuTATE is freely available at GitHub (https://github.com/SarahAyton/MuTATE) under the GPLv3 license.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10287680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An extensive benchmark study on biomedical text generation and mining with ChatGPT. 利用ChatGPT对生物医学文本生成和挖掘进行了广泛的基准研究。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad557

Qijie Chen, Haotong Sun, Haoyang Liu, Yinghui Jiang, Ting Ran, Xurui Jin, Xianglu Xiao, Zhimin Lin, Hongming Chen, Zhangmin Niu

{"title":"An extensive benchmark study on biomedical text generation and mining with ChatGPT.","authors":"Qijie Chen, Haotong Sun, Haoyang Liu, Yinghui Jiang, Ting Ran, Xurui Jin, Xianglu Xiao, Zhimin Lin, Hongming Chen, Zhangmin Niu","doi":"10.1093/bioinformatics/btad557","DOIUrl":"10.1093/bioinformatics/btad557","url":null,"abstract":"Motivation: In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain.Results: To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5.Availability and implementation: All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10173923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 2

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. 位置特异性富集比矩阵得分可从深度测序数据中预测抗体变异特性。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad446

Matthew D Smith, Marshall A Case, Emily K Makowski, Peter M Tessier

{"title":"Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data.","authors":"Matthew D Smith, Marshall A Case, Emily K Makowski, Peter M Tessier","doi":"10.1093/bioinformatics/btad446","DOIUrl":"10.1093/bioinformatics/btad446","url":null,"abstract":"Motivation: Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.Results: Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.Availability and implementation: All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10628969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction. DeepMHCI：一个锚定位置感知的深度相互作用模型，用于准确预测MHC-I肽结合亲和力。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad551

Wei Qu, Ronghui You, Hiroshi Mamitsuka, Shanfeng Zhu

{"title":"DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction.","authors":"Wei Qu, Ronghui You, Hiroshi Mamitsuka, Shanfeng Zhu","doi":"10.1093/bioinformatics/btad551","DOIUrl":"10.1093/bioinformatics/btad551","url":null,"abstract":"Motivation: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels.Results: The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction.Availability and implementation: DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10217795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Reducing cost in DNA-based data storage by sequence analysis-aided soft information decoding of variable-length reads. 序列分析辅助变长读软信息解码降低基于dna的数据存储成本。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad548

Seong-Joon Park, Sunghwan Kim, Jaeho Jeong, Albert No, Jong-Seon No, Hosung Park

{"title":"Reducing cost in DNA-based data storage by sequence analysis-aided soft information decoding of variable-length reads.","authors":"Seong-Joon Park, Sunghwan Kim, Jaeho Jeong, Albert No, Jong-Seon No, Hosung Park","doi":"10.1093/bioinformatics/btad548","DOIUrl":"10.1093/bioinformatics/btad548","url":null,"abstract":"Motivation: DNA-based data storage is one of the most attractive research areas for future archival storage. However, it faces the problems of high writing and reading costs for practical use. There have been many efforts to resolve this problem, but existing schemes are not fully suitable for DNA-based data storage, and more cost reduction is needed.Results: We propose whole encoding and decoding procedures for DNA storage. The encoding procedure consists of a carefully designed single low-density parity-check code as an inter-oligo code, which corrects errors and dropouts efficiently. We apply new clustering and alignment methods that operate on variable-length reads to aid the decoding performance. We use edit distance and quality scores during the sequence analysis-aided decoding procedure, which can discard abnormal reads and utilize high-quality soft information. We store 548.83 KB of an image file in DNA oligos and achieve a writing cost reduction of 7.46% and a significant reading cost reduction of 26.57% and 19.41% compared with the two previous works.Availability and implementation: Data and codes for all the algorithms proposed in this study are available at: https://github.com/sjpark0905/DNA-LDPC-codes.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10631513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms. FunTaxIS-lite:一个简单而轻巧的解决方案，用于研究所有生物体中的蛋白质功能。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad549

Federico Bianca, Emilio Ispano, Ermanno Gazzola, Enrico Lavezzo, Paolo Fontana, Stefano Toppo

{"title":"FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms.","authors":"Federico Bianca, Emilio Ispano, Ermanno Gazzola, Enrico Lavezzo, Paolo Fontana, Stefano Toppo","doi":"10.1093/bioinformatics/btad549","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad549","url":null,"abstract":"Motivation: Defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph.Results: The taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases.Availability and implementation: FunTaxIS-lite is available on https://www.medcomp.medicina.unipd.it/funtaxis-lite and from https://github.com/MedCompUnipd/FunTaxIS-lite.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10631519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

chem16S: community-level chemical metrics for exploring genomic adaptation to environments. chem16S：用于探索基因组对环境适应的社区级化学指标。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad564

Jeffrey M Dick, Xun Kang

引用次数: 0

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes. HAPNEST:高效、大规模地生成和评估基因型和表型的合成数据集。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad535

Sophie Wharrie, Zhiyu Yang, Vishnu Raj, Remo Monti, Rahul Gupta, Ying Wang, Alicia Martin, Luke J O'Connor, Samuel Kaski, Pekka Marttinen, Pier Francesco Palamara, Christoph Lippert, Andrea Ganna

{"title":"HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes.","authors":"Sophie Wharrie, Zhiyu Yang, Vishnu Raj, Remo Monti, Rahul Gupta, Ying Wang, Alicia Martin, Luke J O'Connor, Samuel Kaski, Pekka Marttinen, Pier Francesco Palamara, Christoph Lippert, Andrea Ganna","doi":"10.1093/bioinformatics/btad535","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad535","url":null,"abstract":"Motivation: Existing methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking.Results: We present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures.Availability and implementation: A synthetic dataset of 1 008 000 individuals and nine traits for 6.8 million common variants is available at https://www.ebi.ac.uk/biostudies/studies/S-BSST936. The HAPNEST software for generating synthetic datasets is available as Docker/Singularity containers and open source Julia and C code at https://github.com/intervene-EU-H2020/synthetic_data.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10335851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

MSDRP: a deep learning model based on multisource data for predicting drug response. MSDRP:基于多源数据的深度学习模型，用于预测药物反应。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad514

Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

{"title":"MSDRP: a deep learning model based on multisource data for predicting drug response.","authors":"Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang","doi":"10.1093/bioinformatics/btad514","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad514","url":null,"abstract":"Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines.Results: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model.Availability and implementation: The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10647978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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