Briefings in bioinformatics最新文献

{"title":"EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species.","authors":"Hong-Qi Zhang, Xin-Ran Lin, Yan-Ting Wang, Wen-Fang Pei, Guang-Ji Ma, Ze-Xu Zhou, Ke-Jun Deng, Dan Yan, Tian-Yuan Liu","doi":"10.1093/bib/bbaf249","DOIUrl":"10.1093/bib/bbaf249","url":null,"abstract":"<p><p>With the rapid advancement of proteomics, post-translational modifications, particularly lysine crotonylation (Kcr), have gained significant attention in basic research, drug development, and disease treatment. However, current methods for identifying these modifications are often complex, costly, and time-consuming. To address these challenges, we have proposed EDS-Kcr, a novel bioinformatics tool that integrates the state-of-the-art protein language model ESM2 with deep supervision to improve the efficiency and accuracy of Kcr site prediction. EDS-Kcr demonstrated outstanding performance across various species datasets, proving its applicability to a wide range of proteins, including those from humans, plants, animals, and microbes. Compared to existing Kcr site prediction models, our model excelled in multiple key performance indicators, showcasing superior predictive power and robustness. Furthermore, we enhanced the transparency and interpretability of EDS-Kcr through visualization techniques and attention mechanisms. In conclusion, the EDS-Kcr model provides an efficient and reliable predictive tool suitable for disease diagnosis and drug development. We have also established a freely accessible web server for EDS-Kcr at http://eds-kcr.lin-group.cn/.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198279","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":"Gene Swin transformer: new deep learning method for colorectal cancer prognosis using transcriptomic data.","authors":"Yangyang Wang, Xinyu Yue, Shenghan Lou, Peinan Feng, Binbin Cui, Yanlong Liu","doi":"10.1093/bib/bbaf275","DOIUrl":"10.1093/bib/bbaf275","url":null,"abstract":"<p><p>Transcriptome sequencing has become essential in clinical tumor research, providing in-depth insights into the biology and functionality of tumor cells. However, the vast amount of data generated and the complex relationships between gene expressions make it challenging to effectively identify clinically relevant information. In this study, we developed a method called Gene Swin Transformer to address these challenges. This approach converts transcriptomic data into Synthetic Image Elements (SIEs). We utilized data from 12 datasets, including GSE17536-GSE103479 datasets (n = 1771) and The Cancer Genome Atlas (n = 459), to generate SIEs. These elements were then classified based on survival time using deep learning algorithms to predict colorectal cancer prognosis and build a reliable prognostic model. We trained and evaluated four deep learning models-BeiT, ResNet, Swin Transformer, and ViT Transformer-and compared their performance. The enhanced Swin-T model outperformed the other models, achieving weighted precision, recall, and F1 scores of 0.708, 0.692, and 0.705, respectively, along with area under the curve values of 80.2%, 72.7%, and 76.9% across three datasets. This model demonstrated the strongest prognostic prediction capabilities among those evaluated. Additionally, the PEX10 gene was identified as a key prognostic marker through both visual attention matrix analysis and bioinformatics methods. Our study demonstrates that the Gene Swin model effectively transforms Ribonucleic Acid (RNA) sequencing data into SIEs, enabling prognosis prediction through attention-based algorithms. This approach supports the development of a data-driven, unified, and automated model, offering a robust tool for classification and prediction tasks using RNA sequencing data. This advancement presents a novel clinical strategy for cancer treatment and prognosis forecasting.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293339","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":"Artificial intelligence-driven circRNA vaccine development: multimodal collaborative optimization and a new paradigm for biomedical applications.","authors":"Yan Zhao, Huaiyu Wang","doi":"10.1093/bib/bbaf263","DOIUrl":"10.1093/bib/bbaf263","url":null,"abstract":"<p><p>Circular RNA (circRNA) vaccines have emerged as a groundbreaking innovation in infectious disease prevention and cancer immunotherapy, offering superior stability and reduced immunogenicity compared to conventional linear messenger RNA (mRNA) vaccines. While linear mRNA vaccines are prone to degradation and can trigger strong innate immune responses, covalently closed circRNA vaccines leverage their unique circular structure to enhance molecular stability and minimize innate immune activation, positioning them as a next-generation platform for vaccine development. Artificial intelligence (AI) is revolutionizing circRNA vaccine design and optimization. Deep learning models, such as convolutional neural networks (CNNs) and Transformers, integrate multi-omics data to refine antigen prediction, RNA secondary structure modeling, and lipid nanoparticle delivery system formulation, surpassing traditional bioinformatics approaches in both accuracy and efficiency. While AI-driven bioinformatics enhances antigen screening and delivery system modeling, generative AI accelerates literature synthesis and experimental planning-though the risk of fabricated references and limited biological interpretability hinders its reliability. Despite these advancements, challenges such as the \"black-box\" nature of AI algorithms, unreliable literature retrieval, and insufficient integration of biological mechanisms underscore the necessity for a hybrid \"AI-traditional-experimental\" paradigm. This approach integrates explainable AI frameworks, multi-omics validation, and ethical oversight to ensure clinical translatability. Future research should prioritize mechanism-driven AI models, real-time experimental feedback, and rigorous ethical standards to fully unlock the potential of circRNA vaccines in precision oncology and global health.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246484","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":"Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations.","authors":"Osafu Augustine Egbon, John W Hickey, Benedict Anchang","doi":"10.1093/bib/bbaf277","DOIUrl":"10.1093/bib/bbaf277","url":null,"abstract":"<p><p>Understanding how cells respond to biological perturbations over time and across tissues is key to identifying regulators and networks that inform personalized medicine. Current methods struggle to quantify these dynamic influences in complex multicellular or multitissue systems, especially using single-cell data with spatial and temporal resolution. To address this, we introduce Perturb-STNet, a novel framework that leverages network-based spatiotemporal models to rank spatial and temporal differentially expressed regulators due to perturbation (pSTDERs) driving developmental and disease processes. Perturb-STNet identifies significant pSTDERs, estimates dynamic regulatory networks, and provides detailed visualizations of regulator, cell, and neighborhood interactions critical for understanding disease progression and therapeutic responses. We validated Perturb-STNet using synthetic data and epithelial-to-mesenchymal transition lung cancer data, showing superior performance compared to standard methods. Additionally, we applied it to CODEX single-cell imaging temporal data from a murine melanoma model to study CD8+ T-cell therapy effects, and to MERFISH spatial transcriptomics temporal data to explore inflammation and tissue repair in colitis. In melanoma, Perturb-STNet uncovered regulators like KLRG1 and CD79b, along with mediating pairs and triples (IgD-H2kb, PDL1-H2kb, NKP46-CD117, and FOXP3-CD5-CD25), revealing therapeutic strategies including checkpoint inhibition by targeting PDL1-H2kb to restore CD8+ T cell function, Treg depletion through inhibition of FOXP3-CD5-CD25 axis, and NK cell activation by enhancing NKP46-CD117 interactions. In colitis, Perturb-STNet identified key genes (Csf1r, Col6a1, Lgr4, Myc, and Fzd5) and mediator pairs (Itga5-Flnc, Cd68-Csf1r, Csf1r-Cx3cl1, and Tnfrsf1b-Bmp1) involved in immune regulation, matrix remodeling, and epithelial repair, offering potential therapeutic targets. Overall, Perturb-STNet enables robust identification of spatiotemporal regulatory networks in single-cell perturbation data across diverse disease contexts.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367915","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":"Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.","authors":"Giulia Cesaro, James Shiniti Nagai, Nicolò Gnoato, Alice Chiodi, Gaia Tussardi, Vanessa Klöker, Carmelo Vittorio Musumarra, Ettore Mosca, Ivan G Costa, Barbara Di Camillo, Enrica Calura, Giacomo Baruzzo","doi":"10.1093/bib/bbaf280","DOIUrl":"10.1093/bib/bbaf280","url":null,"abstract":"<p><p>Recent advancements in high-resolution and high-throughput sequencing technologies have significantly enhanced the study of cell-cell communication inference using single-cell and spatial transcriptomics data. Over the past 6 years, this growing interest has led to the development of more than 100 bioinformatics tools and nearly 50 resources, primarily in the form of ligand-receptor databases. These tools vary widely in their requirements, scoring approaches, ability to infer inter- and/or intra-cellular communication, assumptions, and limitations. Similarly, cell-cell communication resources differ in many aspects, mainly in the number of annotated interactions, species coverage, and their focus on inter-cellular signaling or both inter- and intra-cellular communication. This abundance and diversity create challenges in identifying compatible and suitable tools and resources to meet specific user needs. In this collaborative effort, we aim to provide a comprehensive report on the current state of cell-cell communication analysis derived from single-cell or spatial transcriptomics data. The report reviews existing methods and resources, addressing all relevant aspects from the user's perspective. It also explores current limitations, pitfalls, and unresolved issues in cell-cell communication inference, offering an aggregated analysis of the existing literature on the topic. Furthermore, we highlight potential future directions in the field and consolidate the collected knowledge into CCC-Catalog (https://sysbiobig.gitlab.io/ccc-catalog), a centralized web platform designed to serve as a hub for bioinformaticians and researchers interested in cell-cell communication inference.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332454","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":"DeepTFtyper: an interpretable morphology-aware graph neural network for translating histopathology images into molecular subtypes in small cell lung cancer.","authors":"Xin Li, Fan Yang, Yibo Zhang, Zijian Yang, Ruanqi Chen, Meng Zhou, Lin Yang","doi":"10.1093/bib/bbaf284","DOIUrl":"10.1093/bib/bbaf284","url":null,"abstract":"<p><p>Small cell lung cancer (SCLC) is a highly aggressive high-grade neuroendocrine carcinoma with a poor prognosis. Molecular subtyping of transcription factors (SCLC-A, -N, -P, and -Y) shows great potential for guiding treatment decisions. However, its clinical application are limited by insufficient samples and the complexity of molecular testing. In this study, we developed DeepTFtyper, a graph neural network-based deep learning model for automatically classifying SCLC molecular subtypes from hematoxylin and eosin-stained whole-slide images. DeepTFtyper was trained and tested on the Cancer Hospital, Chinese Academy of Medical Science cohort (n = 389) with 4-fold cross-validation, and achieved high performance with an area under the receiver operating characteristic curve above 0.70 for all four molecular subtypes identified by immunohistochemistry (IHC). Furthermore, the digital H-scores predicted by DeepTFtyper showed a significant correlation with IHC-based H-scores. Patch-level visualization and morphological analysis revealed that DeepTFtyper identifies interpretable and generalizable features corresponding to areas of relevant transcription factor expression as revealed by IHC staining and correlates well with morphological features. This study represents the first deep learning framework for predicting SCLC molecular subtypes from hematoxylin and eosin-stained histology slides, providing a scalable, accurate, and clinically relevant tool to improve patient management and guide personalized treatment decisions.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332455","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":"mKmer: an unbiased K-mer embedding of microbiomic single-microbe RNA sequencing data.","authors":"Fangyu Mo, Qinghong Qian, Xiaolin Lu, Dihuai Zheng, Wenjie Cai, Jie Yao, Hongyu Chen, Yujie Huang, Xiang Zhang, Sanling Wu, Yifei Shen, Yinqi Bai, Yongcheng Wang, Weiqin Jiang, Longjiang Fan","doi":"10.1093/bib/bbaf227","DOIUrl":"10.1093/bib/bbaf227","url":null,"abstract":"<p><p>The advanced single-microbe RNA sequencing (smRNA-seq) technique addresses the pressing need to understand the complexity and diversity of microbial communities, as well as the distinct microbial states defined by different gene expression profiles. Current analyses of smRNA-seq data heavily rely on the integrity of reference genomes within the queried microbiota. However, establishing a comprehensive collection of microbial reference genomes or gene sets remains a significant challenge for most real-world microbial ecosystems. Here, we developed an unbiased embedding algorithm utilizing K-mer signatures, named mKmer, which bypasses gene or genome alignment to enable species identification for individual microbes and downstream functional enrichment analysis. By substituting gene features in the canonical cell-by-gene matrix with highly conserved K-mers, we demonstrate that mKmer outperforms gene-based methods in clustering and motif inference tasks using benchmark datasets from crop soil and human gut microbiomes. Our method provides a reference genome-free analytical framework for advancing smRNA-seq studies.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12100620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126698","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":"PYF: a multi-functional algorithm for predicting production and optimizing metabolic engineering strategy in Escherichia coli microbial consortia.","authors":"Chen Yang, Yingqi Zhao, Boyuan Xue, Shaojie Wang, Haijia Su","doi":"10.1093/bib/bbaf295","DOIUrl":"10.1093/bib/bbaf295","url":null,"abstract":"<p><p>Simulating production in microbial consortia is crucial for optimizing metabolic engineering strategies to achieve high yields. However, existing algorithms for modeling polymicrobial metabolic fluxes, based on genome-scale metabolic networks, often overlook the conflicts and coordination between biosynthesis tasks and self-growth interests, leading to limited prediction accuracy. This study introduces the Polymicrobial cell factory Yield Forecasting (PYF) algorithm, which simulates the relationships between biosynthesis and growth more effectively by incorporating the expression degrees of biosynthesis pathways. PYF was shown to accurately predict the production of Escherichia coli-E. coli consortia under various scenarios, including mono-metabolite exchange, dual-carbon sources, and dual-metabolite exchange. The results revealed a mean relative error (MRE) of 0.106, an average determination coefficient of 0.883, and an average hypothesis testing parameter of 0.930 between predicted and experimental productions. Compared with the recent metabolic simulation algorithm, PYF reduced the MRE by ~61.6%. PYF is adaptable and enables accurate simulation even without enzyme catalytic data. Meanwhile, PYF rapidly analyzed and optimized metabolic engineering strategies through sensitivity analysis. By eliminating the need for specialized division and integration of polymicrobial metabolic networks, PYF greatly simplifies the simulation process, offering a novel approach for predicting and enhancing production in microbial consortia.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339900","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":"Graph-RPI: predicting RNA-protein interactions via graph autoencoder and self-supervised learning strategies.","authors":"Jiahui Guan, Lantian Yao, Peilin Xie, Zhihao Zhao, Dian Meng, Tzong-Yi Lee, Junwen Wang, Ying-Chih Chiang","doi":"10.1093/bib/bbaf292","DOIUrl":"10.1093/bib/bbaf292","url":null,"abstract":"<p><p>RNA-protein interactions (RPIs) are essential for many biological functions and are associated with various diseases. Traditional methods for detecting RPIs are labor-intensive and costly, necessitating efficient computational methods. In this study, we proposed a novel sequence-based RPI prediction framework based on graph neural networks (GNNs) that addressed key limitations of existing methods, such as inadequate feature integration and negative sample construction. Our method represented RNAs and proteins as nodes in a unified interaction graph, enhancing the representation of RPI pairs through multi-feature fusion and employing self-supervised learning strategies for model training. The model's performance was validated through five-fold cross-validation, achieving accuracy of 0.880, 0.811, 0.950, 0.979, 0.910, and 0.924 on the RPI488, RPI369, RPI2241, RPI1807, RPI1446, and RPImerged datasets, respectively. Additionally, in cross-species generalization tests, our method outperformed existing methods, achieving an overall accuracy of 0.989 across 10 093 RPI pairs. Compared with other state-of-the-art RPI prediction methods, our approach demonstrates greater robustness and stability in RPI prediction, highlighting its potential for broad biological applications and large-scale RPI analysis.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473961","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":"iGTP: learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.","authors":"Kang-Lin Hsieh, Kai Zhang, Yan Chu, Lishan Yu, Xiaoyang Li, Nuo Hu, Isha Kawosa, Patrick G Pilié, Pratip K Bhattacharya, Degui Zhi, Xiaoqian Jiang, Zhongming Zhao, Yulin Dai","doi":"10.1093/bib/bbaf296","DOIUrl":"10.1093/bib/bbaf296","url":null,"abstract":"<p><p>Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states. We demonstrated the performance of iGTP in a diverse biological context using gene ontology, canonical pathway, and different PPI curation. iGTP not only elucidated the ground truth of cellular responses but also surpassed other deep learning models and traditional bioinformatics methods in functional enrichment tasks. By integrating the latent layer with a graph neural network framework, iGTP could effectively infer cellular responses to perturbations. Lastly, we applied iGTP TP embeddings with a latent diffusion model to accurately generate cell embeddings for specific cell types and states. We anticipate that iGTP will offer insights at both PPI and TP levels and holds promise for predicting responses to novel perturbations.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473963","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}