Briefings in bioinformatics最新文献

{"title":"The bioinformatics of the finding that the hepatitis delta virus RNA editing mechanism by a conformational switch exists in genotype 7 in addition to genotype 3.","authors":"Rami Zakh, Alexander Churkin, Marina Parr, Tamir Tuller, Ohad Etzion, Harel Dahari, Danny Barash","doi":"10.1093/bib/bbaf451","DOIUrl":"10.1093/bib/bbaf451","url":null,"abstract":"<p><p>Hepatitis delta virus (HDV) is geographically classified according to eight known genotypes. The combined hepatitis B-hepatitis D (HEPB-HEPD) disease is the severest form of chronic viral hepatitis in humans and is characterized by mortality rates of ~20%. Hepatitis delta virus has no FDA approved therapy and its only available vaccine is the one for HEPB. Because it is the smallest RNA virus known to infect humans, RNA folding predictions by energy minimization of the whole genome can reveal important information on functional RNA secondary structure elements within the genome. A public HDV database (HDVdb) contains 512 HDV strains on which various bioinformatics methods can be applied, aiming to detect strains that could perform RNA editing via conformational switching. Up to date, only one such strain from HDVdb was known to perform that, in HDV genotype 3. Our goal was to locate more such strains, both in genotype 3 and in other possible HDV genotypes. In past work, by an eigenvalue mathematical analysis, we made an initial prediction that this peculiar RNA editing mechanism also exists in HDV genotype 7. We hereby extend our earlier findings and present newly discovered HDV strains from multiple genotypes for further analysis of RNA editing sites within the virus. The relevant strains taken from HDVdb are from both genotype 3 of Peru and genotype 7 of Cameroon. Additionally, the new strains have a variety of optional RNA editing sites that we report, many of which are unknown to date.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12406691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144943433","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}

{"title":"TMBquant: an explainable AI-powered caller advancing tumor mutation burden quantification across heterogeneous samples.","authors":"Shenjie Wang, Xiaonan Wang, Xiaoyan Zhu, Xuwen Wang, Yuqian Liu, Minchao Zhao, Zhili Chang, Yang Shao, Haitao Zhang, Shuanying Yang, Jiayin Wang","doi":"10.1093/bib/bbaf455","DOIUrl":"10.1093/bib/bbaf455","url":null,"abstract":"<p><p>Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets. We benchmarked TMBquant against nine widely used variant callers, including traditional tools (e.g. Mutect2, VarScan2, Strelka2) and recent AI-based methods (DeepSomatic, Octopus), using 706 whole-exome sequencing tumor-control pairs. To evaluate clinical relevance, we further assessed TMBquant through survival analyses across immunotherapy-treated cohorts of non-small cell lung cancer (NSCLC), nasopharyngeal carcinoma (NPC), and the two NSCLC subtypes: lung adenocarcinoma and lung squamous cell carcinoma. In each cohort, TMBquant consistently achieved the highest hazard ratios, demonstrating superior patient stratification compared to all other methods. Importantly, TMBquant maintained robust predictive performance across both high-TMB (NSCLC) and low-TMB (NPC) settings, highlighting its generalizability across cancer types with distinct biological characteristics. These findings establish TMBquant as a reliable, reproducible, and clinically actionable tool for precision oncology. The software is open source and freely available at https://github.com/SomaticCaller/SomaticCaller. To enhance reproducibility, we provide detailed usage instructions and representative code snippets for TMBquant in the Methods section (see Code Availability).</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12415849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013810","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}

{"title":"IBI-DT: a novel approach combining individualized Bayesian inference and decision tree for identifying cancer drivers and their interactions.","authors":"Md Asad Rahman, Gregory F Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu","doi":"10.1093/bib/bbaf463","DOIUrl":"10.1093/bib/bbaf463","url":null,"abstract":"<p><p>Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing smaller subgroups with similar genetic makeup (i.e. patient-like-me subgroups) using a decision tree structure and analyzing multiple trees to identify the SGAs that play a significant role in regulating downstream gene expression patterns at the subgroup and individual levels. This is distinct from population-based approaches, which tend to evaluate the influence of an SGA for the entire population, thereby likely missing low-frequency SGAs that may well explain a small subgroup of cancer patients. Also importantly, IBI-DT can efficiently identify cancer drivers that may have functional interactions. We applied IBI-DT to identify cancer drivers regulating the downstream differential gene expression in cancer patients and compared it to the standard, population-based method of expression quantitative trait loci analysis. Our results show that IBI-DT performs well in identifying both important cancer drivers, especially the low-frequency drivers, and their interactions, allowing for a better understanding of the cancer signaling pathways.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12450346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102495","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}

{"title":"Predicting response and survival of lung adenocarcinoma under anti-programmed death-1 therapy using biological deep learning.","authors":"Yuanyuan Wang, Liuchao Zhang, Hongyu Xie, Liuying Wang, Yaru Wang, Shuang Li, Jia He, Meng Wang, Xuan Zhang, Hesong Wang, Kang Li, Lei Cao","doi":"10.1093/bib/bbaf479","DOIUrl":"10.1093/bib/bbaf479","url":null,"abstract":"<p><p>Although programmed death (PD)-1 inhibitors inhibitors have been clinically approved for the treatment of lung adenocarcinoma (LUAD), only a few patients benefit from anti-PD-1 therapy. We developed a semi-supervised biological sparse neural network (sBiosNet) based on transfer learning to fully utilize labeled and unlabeled patient data. The pathways from the Reactome database were used to sparse the sBiosNet and extract associated biological features by integrating patients' genomic mutations and copy number variation data. We assessed the performance of the sBiosNet against random forest and support vector machine using four cohorts and provided clear interpretations using the DeepLIFT algorithm. The sBiosNet achieved the best prediction with an area under the receiver operating characteristic curve (AUROC) of 0.888 and an area under the precision recall curve (AUPR) of 0.919 for responders versus non-responders on the validation cohort, and AUROC of 0.853 and AUPR of 0.894 on an independent external cohort. The ablation experiments demonstrated that biological sparsification and multi-omics data integration, transfer learning and semi-supervised learning all contributed to improving the sBiosNet's performance. We further confirmed that genes (such as TP53, FGF3, FGFR4, and EGFR) affected LUAD patients' response to PD-1 inhibitors by regulating pathways. Meanwhile, the Low-risk LUAD patients identified by the sBiosNet obtained significant longer overall survival and progression-free survival with anti-PD-1 therapy. In conclusion, the sBiosNet accurately predicts the response and survival of patients on anti-PD-1 therapy to reduce unnecessary treatment in non-responders.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091282","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}

{"title":"MINERVA-microbiome network research and visualization atlas: a scalable knowledge graph for mapping microbiome-disease associations.","authors":"Saul Langarica, Young-Tak Kim, Adham Alkhadrawi, Jung Bin Kim, Synho Do","doi":"10.1093/bib/bbaf472","DOIUrl":"10.1093/bib/bbaf472","url":null,"abstract":"<p><p>Bacterial pathogens contribute significantly to the global burden of disease. Understanding their complex interactions with human health is essential for developing new diagnostic, preventative, and therapeutic strategies. While recent breakthroughs have revolutionized our understanding of these relationships, the rapid expansion of microbiome research presents a significant challenge: knowledge remains scattered across scientific literature, hindering comprehensive analysis and clinical translation. To address this, we introduce MINERVA (Microbiome Network Research and Visualization Atlas), an innovative platform that leverages a fine-tuned large language model to systematically map microbe-disease associations across extensive scientific literature. MINERVA constructs a rich, ontology-driven knowledge graph that prioritizes accuracy and transparency, enabling efficient exploration and discovery of previously hidden associations relevant to clinical decision-making. The platform features specialized modules that allow researchers to analyze individual microbes and diseases, visualize complex relationships within the knowledge network, uncover hidden connections through advanced graph algorithms and machine-learning models, and perform personalized and population-level microbiome compositional analysis. These capabilities facilitate the identification of disease risks, comorbidities, and actionable insights, supporting both research and clinical decision-making. By bridging the gap between microbiome research and real-world applications, MINERVA has the potential to transform our understanding of microbe-disease interactions, accelerating discoveries and advancing patient care. The MINERVA platform is available at https://minervabio.org/.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124203","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}

{"title":"Identifying survival subtypes with autoencoder using multiple types of high-dimensional genomic data from studies of glioblastoma multiforme.","authors":"Imran Parvez, Jie Chen","doi":"10.1093/bib/bbaf499","DOIUrl":"10.1093/bib/bbaf499","url":null,"abstract":"<p><p>Analysis of multiple types of omics data facilitates a comprehensive revelation of molecular-level complexity and interactions among genomic features. This knowledge promotes the development of new therapies for treating different genomic diseases. An integrative study of multiple types of genomic data instead of a single type of genomic data will be more informative in understanding the complicated molecular activities and their interactions. In this work, we integrated RNA-sequencing (RNA-seq), methylation, and DNA copy number variation data, downloaded from the TCGA public repository, of glioblastoma multiforme (GBM), reduced the dimension of these high-dimensional genomic data using an autoencoder, a deep learning-based method, and then used Cox-PH model to select the autoencoder-transformed features that have a significant contribution to patient survival. We utilized the significant set of autoencoder-transformed features to classify the survival subtypes using the integrated data. We built a classification model with a penalization technique, sparse group LASSO, and evaluated the approach using cross-validation. As a result, two survival subgroups, with overall different survival profiles and linking to various genomic features, are discovered for respective GBM patients. Finally, the results are interpreted biologically by differential expression analysis and pathway analysis.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184465","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}

{"title":"RMR-ICP: robust Mendelian randomization method accounting for idiosyncratic and correlated pleiotropy with applications to stroke outcomes.","authors":"Qing Cheng, Wenxin Xu, Chan Wang, Jin Liu, Yanyan Zhao","doi":"10.1093/bib/bbaf508","DOIUrl":"10.1093/bib/bbaf508","url":null,"abstract":"<p><strong>Motivation: </strong>Mendelian randomization (MR) serves as a valuable tool for investigating causal relationships between exposures and disease outcomes in observational studies. However, MR methods, operating under classical assumptions, may yield biased estimates and inflated false-positive causal relationships when faced with realistic and complex correlated horizontal pleiotropy (CHP). While numerous MR methods have emerged to address CHP effects, limited methods can effectively handle relatively large direct effects, commonly known as idiosyncratic pleiotropy.</p><p><strong>Results: </strong>To address this gap, we propose an efficient and robust MR method to account for idiosyncratic and correlated pleiotropy, named RMR-ICP. Furthermore, Our method incorporates linkage disequilibrium structure using paralleled Gibbs sampling to enhance statistical power. The robustness and efficiency of our method are demonstrated through extensive simulation studies and applications. RMR-ICP is first used to analyze the effects of plasma proteins on stroke, followed by its application to conventional stroke risk factors. Our analysis reveals that Selectin E (SELE) exhibits a positive causal effect on the occurrence of any stroke. Only those specifically designed to account for idiosyncratic and CHP identified a significant positive causal effect of myeloperoxidase on ischemic stroke, with RMR-ICP providing stronger statistical evidence. Elevated Natriuretic Peptide B (BNP) levels specifically increase the risk of cardioembolic stroke (CES), though not with other stroke subtypes. This finding is consistent with previous studies suggesting that plasma BNP levels may help distinguish CES from other stroke types. Higher Waist-hip ratio (WHR) levels raise the risk across all stroke types. These findings provide new insights into identifying stroke-related risk factors.</p><p><strong>Availability and implementation: </strong>RMR-ICP is publicly available at https://github.com/QingCheng0218/RMR.ICP.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184468","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}

{"title":"A novel pairwise sequence alignment algorithm for similarity search in massive datasets.","authors":"Yosef Masoudi-Sobhanzadeh, Yadollah Omidi","doi":"10.1093/bib/bbaf512","DOIUrl":"10.1093/bib/bbaf512","url":null,"abstract":"<p><p>Advances in sequencing technologies have resulted in the production of a huge volume of data. Since the pairwise sequence alignment plays an essential role in comparing sequencing data, various algorithms have been developed. Among the previously suggested algorithms, the basic local alignment search tool (BLAST) is currently employed in a wide range of biological applications, largely due to its low time and memory complexity. However, not only BLAST but also other improved sequence alignment algorithms may fail to produce accurate results, therefore, more efficient algorithms can be highly advantageous. In the present study, we introduce a novel algorithm for sequence alignment (NASA) consisting of preprocessing and aligning steps. In the preprocessing step, the positions of residues are determined within a provided nucleotide or peptide sequence, resulting in seeking only informative regions. In the aligning step, based on a constant number of comparisons, the sequence similarity score is calculated between two sequences in a linear time and memory orders. To evaluate NASA, a large volume of sequencing data was analyzed and the outcomes were compared with other algorithms. The results showed that NASA outperforms other basic algorithms in terms of the elapsed time, required memory, system resource utilization, and alignment score precision. Collectively, NASA might be a promising method for retrieving similar sequences from large datasets.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184718","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}

{"title":"Toward high-efficiency, low-resource, and explainable neuropeptide prediction with MSKDNP.","authors":"Peilin Xie, Jiahui Guan, Zhihao Zhao, Yulan Liu, Zhang Cheng, Xuxin He, Xingchen Liu, Yun Tang, Zhenglong Sun, Tzong-Yi Lee, Lantian Yao, Ying-Chih Chiang","doi":"10.1093/bib/bbaf466","DOIUrl":"10.1093/bib/bbaf466","url":null,"abstract":"<p><p>Neuropeptides are essential signaling molecules produced in the nervous system that regulate diverse physiological processes and are closely implicated in the pathogenesis of neurodegenerative and neuropsychiatric disorders. Investigating neuropeptides contributes to a better understanding of their regulatory mechanisms and offers new insights into therapeutic strategies for related diseases. Therefore, accurate identification of neuropeptides is crucial for advancing biomedical research and drug development. Due to the high cost of experimental validation, various artificial intelligence methods have been developed for rapid neuropeptide identification. However, existing approaches often suffer from high computational resource consumption, slow processing speed, and poor deploy ability. Moreover, a user-friendly web server for practical application is still lacking. To this end, we propose MSKDNP, a neuropeptide prediction model based on a multi-stage knowledge distillation framework. With only 1.2% of the parameters, MSKDNP attains performance comparable to a fully fine-tuned protein language model while achieving state-of-the-art results in neuropeptide recognition. Moreover, MSKDNP provides favorable interpretability, facilitating biological understanding. A freely accessible web server is available at https://awi.cuhk.edu.cn/∼biosequence/MSKDNP/index.php.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085159","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}

{"title":"Computational refinement and multivalent engineering of complementarity-determining region-grafted nanobodies on a humanized scaffold for retaining antiviral efficacy.","authors":"Liyun Huo, Qin Qin, Tian Tian, Xing Zhang, Xiaoming He, Yuhui Cao, Tianfu Zhang, Yanqin Xu, Qiang Huang","doi":"10.1093/bib/bbaf477","DOIUrl":"10.1093/bib/bbaf477","url":null,"abstract":"<p><p>Recently, nanobody-based therapeutics have emerged as a highly effective strategy for COVID-19 treatment. However, camelid-derived nanobodies often require humanization engineering to reduce immunogenicity in clinical applications while simultaneously preserving their target-binding affinities. Here, we employed a computational and engineering approach to optimize the binding affinities of complementarity-determining region (CDR)-grafted humanized variants of the camelid-derived nanobody Nb2-67, which exhibits potent SARS-CoV-2 neutralization. By grafting the three CDR loops of Nb2-67 onto the humanized scaffold of the approved therapeutic nanobody Caplacizumab and refining the target-binding interface, we generated five nanobody variants with improved computational humanness scores. Three of these variants (Nb491, Nb273, and Nb1052) retained neutralizing activity. To further enhance their potency, we fused these variants to a self-assembling scaffold, generating three multivalent constructs with higher humanness scores. Pseudovirus assays showed that all the trivalent nanobodies exhibited picomolar neutralizing potency comparable to the original trivalent Nb2-67. Our study presents a novel computational and multivalent engineering strategy that effectively restores the antiviral efficacy of humanized CDR-grafted nanobody variants, offering a valuable approach for developing nanobody-based therapeutics against COVID-19 and other diseases.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085089","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}