Briefings in bioinformatics最新文献_第10页

Drug screening for α-synuclein aggregation inhibitors via multimodal graph neural network. 基于多模态图神经网络的α-突触核蛋白聚集抑制剂药物筛选。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag118

Tingle Gu, Zixu Ran, Wenyin Li, Xudong Guo, Bo Li, Fuyi Li, Cangzhi Jia

{"title":"Drug screening for α-synuclein aggregation inhibitors via multimodal graph neural network.","authors":"Tingle Gu, Zixu Ran, Wenyin Li, Xudong Guo, Bo Li, Fuyi Li, Cangzhi Jia","doi":"10.1093/bib/bbag118","DOIUrl":"10.1093/bib/bbag118","url":null,"abstract":"The pathological aggregation of α-synuclein (α-syn) constitutes a pivotal hallmark in the progression of neurodegenerative disorders, including Parkinson's disease, underscoring the imperative need for identifying site-specific ligands. This study presents, for the first time, an advanced deep learning framework specifically designed for the prediction of molecular properties associated with α-syn. The framework integrates graph-based contextual attention mechanisms, structural feature aggregation protocols, and dual-channel feature integration, complemented by a composite regularization strategy that synergizes mean squared error minimization, Kullback-Leibler divergence-induced latent space regularization, and L2 norm penalization, thereby delivering outstanding predictive accuracy on the independent test dataset with MSE of 0.1812. Mechanistic insights derived from GNNExplainer analysis and molecular docking studies (PDB: 6A6B) elucidated that aromatic ring systems (benzene ring significance: 0.737) and hydrogen bond donor groups (amino group significance: 0.438) play critical roles in mediating high-affinity ligand-receptor interactions through π-π stacking within the hydrophobic pocket formed by Val82 and Ala89 residues, as well as directed hydrogen bonding involving catalytic residues Ser42 and Lys45. These findings not only enhance the understanding of inhibitor mechanisms but also establish a novel framework for the preliminary screening of small-molecule therapeutics, thereby laying a rigorous groundwork for structure-guided drug optimization and rational molecular design.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13006971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147497677","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}

引用次数: 0

Response to "Addressing biases and limitations in feature attribution for circRNA modification profiling". 对“解决circRNA修饰谱特征归属的偏见和限制”的回应。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag161

Jiayi Li, Shenglun Chen, Zhixing Wu, Haozhe Wang, Rong Xia, Jia Meng, Yuxin Zhang

引用次数: 0

MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction. MELGene：用于疾病-基因关联预测的知识增强多模型集成学习。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag172

Haoyu Tian, Kuo Yang, Zeyu Liu, Hong Gao, Jian Yu, Lei Zhang, Xuezhong Zhou

{"title":"MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction.","authors":"Haoyu Tian, Kuo Yang, Zeyu Liu, Hong Gao, Jian Yu, Lei Zhang, Xuezhong Zhou","doi":"10.1093/bib/bbag172","DOIUrl":"10.1093/bib/bbag172","url":null,"abstract":"Disease-gene prediction (DGP) plays a pivotal role in understanding the genetic underpinnings of various diseases, offering insights for disease diagnosis, treatment, and prevention. Accurate identification of disease-related genes can enhance personalized medicine and the development of targeted therapies. While numerous methods for DGP have been proposed in the field, a significant challenge remains in effectively capturing and modeling the complex relationships among biological entities, such as diseases, symptoms, genes, and pathways. These intricate interactions are essential for learning robust representations of phenotypes and genotypes, which are critical for accurate DGP. In this study, we introduce MELGene, a knowledge-enhanced multimodel ensemble learning framework for DGP. MELGene leverages an adaptive integration of multiple pretrained knowledge inference models based on knowledge graph, effectively integrating the collective intelligence of diverse models to achieve more accurate gene predictions. The framework incorporates Model-aware Importance Learning, which dynamically adjusts the contributions of individual models, and introduces a dynamic ensemble mechanism to obtain robust consensus predictions. Finally, we conducted comprehensive experiments, including performance comparisons, which demonstrated the excellent performance of MELGene. Ablation experiments highlighted the positive impact of each module, while case studies showcased the reliability of the biological relevance of gastric, lung, and liver cancers, as supported by the analysis of network medicine, functional enrichment, and literature mining. MELGene offers a flexible framework for DGP through knowledge enhancement and adaptive ensemble learning, with broad potential for decoding disease mechanisms.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147688017","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}

引用次数: 0

Integrating and mapping single-cell transcriptomics across the entire gene expression space. 整合和绘制单细胞转录组学在整个基因表达空间。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag204

Shuzhen Ding, Xintong Zhai, Zhou Yu, Jingsi Ming

{"title":"Integrating and mapping single-cell transcriptomics across the entire gene expression space.","authors":"Shuzhen Ding, Xintong Zhai, Zhou Yu, Jingsi Ming","doi":"10.1093/bib/bbag204","DOIUrl":"10.1093/bib/bbag204","url":null,"abstract":"The exponential growth of single-cell transcriptomics datasets has made it essential to integrate heterogeneous datasets for constructing large-scale single-cell reference atlases and mapping query datasets onto these references. However, this integration process is significantly hampered by batch effects, which introduce systematic biases and mask the true biological signals. Moreover, most existing integration methods are mainly limited to the latent space of highly variable genes, restricting their capacity to comprehensively correct the entire transcriptomic landscape and potentially overlooking crucial biological information encoded in genes with lower variability. We introduce scGES, a novel deep learning framework designed to effectively correct batch effects across the entire gene expression space, which leverages information from both highly and lowly variable genes. scGES consists of two main models: scGESI for data integration and scGESM for query mapping. Comprehensive analyses of real data demonstrate that scGES outperforms state-of-the-art methods in batch effect correction and biological variation conservation, thereby enhancing downstream analyses and offering broader biological insights by utilizing information from all genes.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13130072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147762459","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}

引用次数: 0

Confidence scoring for deep learning-predicted antibody-antigen complexes: AntiConf as a precision-driven metric. 深度学习预测抗体-抗原复合物的置信度评分：AntiConf作为精度驱动的度量。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag137

Serbülent Ünsal, Benjamin Holland, Inci Sardag, Emel Timucin

{"title":"Confidence scoring for deep learning-predicted antibody-antigen complexes: AntiConf as a precision-driven metric.","authors":"Serbülent Ünsal, Benjamin Holland, Inci Sardag, Emel Timucin","doi":"10.1093/bib/bbag137","DOIUrl":"10.1093/bib/bbag137","url":null,"abstract":"Accurate determination of antibody-antigen (Ab-Ag) complex structures is critical for therapeutic development. While deep learning-based methods, beginning with AlphaFold2 (AF2), have revolutionized multimer predictions, the optimal strategies for Ab-Ag modeling, and the reliability of their confidence scores remain active areas of research. This study evaluates the performance of AF2, Boltz-1, Boltz-1x, Boltz-2, Chai-1, Protenix, Protenix-1, OpenFold3, and ESMFold, on a curated dataset of 200 Ab-Ag complexes. Among the nine methods tested, Protenix-1 emerged as the top performer, with Chai-1 consistently ranking second across multiple success metrics, closely followed by AF2. We observed diverse effects of recycling iterations, with AF2, Chai-1, and Protenix variants benefiting from increased cycles, unlike Boltz variants. We analyzed various model confidence scores, noting high precision from pDockQ2 and high recall from predicted Template-Modeling (pTM) score. By integrating these two scores, we developed antibody confidence (AntiConf), a novel metric that achieves superior performance for all methods in terms of precision and recall. These strengths make AntiConf a valuable post score for both computational predictions and downstream experimental workflows, reflecting its potential to improve Ab-Ag complex predictions by AF2 and AF3 architectures. Altogether, this study addresses current limitations in deep learning-based Ab-Ag complex prediction, showcasing the potential of AntiConf for future assessment studies, and providing a guideline for improving the accuracy of Ab-Ag complex prediction.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13032827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572321","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}

引用次数: 0

Castl: robust identification of spatially variable genes in spatial transcriptomics via an ensemble-based framework. 通过基于集成的框架在空间转录组学中识别空间可变基因。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag074

Yiyi Yu, Jiyuan Yang, Ping-An He, Xiaoqi Zheng

{"title":"Castl: robust identification of spatially variable genes in spatial transcriptomics via an ensemble-based framework.","authors":"Yiyi Yu, Jiyuan Yang, Ping-An He, Xiaoqi Zheng","doi":"10.1093/bib/bbag074","DOIUrl":"10.1093/bib/bbag074","url":null,"abstract":"Spatially variable genes (SVGs) are essential for elucidating tissue organization within spatially resolved transcriptomics. While a number of computational methods have been developed for SVG identification, their reliance on algorithm-specific assumptions, such as predefined kernel functions or spatial neighborhood graphs, often results in substantial variability in sensitivity and inflated false discovery rates (FDRs) across heterogeneous datasets. To address this challenge, we here develop Castl, an ensemble-based framework for SVG identification that integrates multiple detection methods through statistically designed aggregation modules. Comprehensive evaluations on both simulated and real-world data demonstrate that Castl consistently identifies biologically meaningful spatial expression patterns, mitigates method-specific biases and effectively controls FDRs across various biological contexts, resolutions, and spatial technologies. This flexible, assumption-free framework offers a robust and standardized foundation for spatially informed feature discovery in complex biological systems.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12963980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147364150","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}

引用次数: 0

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag094

Jing Wang, Junfeng Xia, Yansen Su, Chun-Hou Zheng

{"title":"scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.","authors":"Jing Wang, Junfeng Xia, Yansen Su, Chun-Hou Zheng","doi":"10.1093/bib/bbag094","DOIUrl":"10.1093/bib/bbag094","url":null,"abstract":"The development of single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for elucidating cell heterogeneity and gene expression. Identifying and discovering cell types through cell clustering is a crucial step in analyzing scRNA-seq data. However, the high-dimensionality nature and frequent dropout events of the data raise great challenges for cell clustering. Here, we propose a novel contrastive clustering framework called scSCCNIA (Similarity-matrix-based Contrastive Clustering with Neighbor Information Aggregation), for the accurate identification of cell clusters from scRNA-seq data. scSCCNIA adopts a Laplacian filter to conduct neighbor information aggregation, constructs different graph views by using special un-shared parameters Siamese encoders for data augmentation, and learns the latent low-dimensional embedding representations via similarity-matrix-based contrastive learning. Comparative analyses of multiple scRNA-seq datasets from different platforms and with varying cell numbers demonstrate that scSCCNIA outperforms existing methods in terms of cell clustering and marker gene identification. Furthermore, scSCCNIA reveals the heterogeneity and functional specificity of various cell types through Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Overall, scSCCNIA is an effective algorithm for learning latent features from scRNA-seq data, enhancing cell type identification accuracy and facilitating downstream analyses of scRNA-seq data.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12962064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147364179","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}

引用次数: 0

A progressive fine-tuning framework with dynamic parameter selection for low-resource peptide-GPCR interaction prediction. 基于动态参数选择的渐进式微调框架用于低资源多肽- gpcr相互作用预测。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag116

Mingqing Liu, Jinhui Xu, Ji Liu

{"title":"A progressive fine-tuning framework with dynamic parameter selection for low-resource peptide-GPCR interaction prediction.","authors":"Mingqing Liu, Jinhui Xu, Ji Liu","doi":"10.1093/bib/bbag116","DOIUrl":"10.1093/bib/bbag116","url":null,"abstract":"G protein-coupled receptors (GPCRs) are among the most important drug targets, and peptide therapeutics are rapidly emerging. However, accurate prediction of peptide-GPCR interactions (PepGI) remains challenging due to the scarcity of high-quality data and the poor generalization of existing drug-target interaction (DTI) models, which are largely trained on small molecule data. Here, we introduce a progressive fine-tuning framework with a dynamic parameter selection strategy that adaptively selects critical fine-tuning parameters using Fisher information. Our method begins with pretraining on a large small molecule-GPCR dataset, followed by intermediate fine-tuning on peptide-target data to alleviate the representation mismatch across heterogeneous ligand modalities. Finally, the task-specific fine-tuning is performed on the low-resource PepGI scenario. Extensive experiments show that our approach significantly outperforms baselines across multiple evaluation metrics, and exhibits robust generalization under few-shot and practical cold-start settings. Overall, this work offers an effective solution for low-resource peptide-GPCR prediction and presents a transferable framework for cross-structure DTI modeling.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12991051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147466888","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}

引用次数: 0

A bio-inspired computational pipeline for antibody screening and repurposing. 一个生物启发的抗体筛选和再利用的计算管道。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag183

Junxin Li, Mark A Ige, Chao Zhang, Linbu Liao, Faiz Rasul, Xiaohu Ren, Xiaochun Wan, Youhai Chen, Haiping Zhang

{"title":"A bio-inspired computational pipeline for antibody screening and repurposing.","authors":"Junxin Li, Mark A Ige, Chao Zhang, Linbu Liao, Faiz Rasul, Xiaohu Ren, Xiaochun Wan, Youhai Chen, Haiping Zhang","doi":"10.1093/bib/bbag183","DOIUrl":"10.1093/bib/bbag183","url":null,"abstract":"Therapeutic antibody discovery is central to modern drug development, yet conventional methods such as hybridoma and phage display remain slow, inefficient, and costly. Computational approaches including site-saturation mutagenesis often yield limited affinity gains and expression liabilities, while deep learning and generative models expand sequence diversity but suffer from low validation rates. Here, we present a multi-scale computational screening pipeline inspired by key principles of in vivo immune selection. The framework integrates structure-based docking (ZDock), graph neural network-based interaction prediction, and accelerated molecular dynamics (MDs) with metadynamics free-energy profiling to enable high-throughput in silico prioritization of structure-resolved antibodies. Applied to Activin A, a pleiotropic cytokine implicated in fibrosis, oncology, and muscle-wasting disorders, the platform screened ~5000 antibody structures and identified 11 candidates. Experimental validation confirmed two binders, with Ab4 exhibiting sub-nanomolar affinity (KD = 0.38 nM) and potent neutralizing activity, underscoring therapeutic potential in fibrodysplasia ossificans progressiva (FOP) and related diseases. Rather than performing full iterative affinity maturation, the present study focuses on the screening and repurposing stage, with affinity maturation positioned as a prospective extension. This work demonstrates the feasibility of integrating AI-driven interaction prediction with physics-based simulations to accelerate structure-guided antibody screening and repurposing, while conceptually paralleling selected stages of immune selection rather than fully recapitulating immune evolution.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147687975","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}

引用次数: 0

TF-loop: deciphering the transcription factor regulatory language for CTCF-mediated chromatin loop based on BERT. tf环：基于BERT解读ctcf介导的染色质环转录因子调控语言。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2026-03-01 DOI: 10.1093/bib/bbag162

Yi-Xuan Qi, Hao-Jiang Zhang, Hao-Xiang Tang, Zi-Xuan Zhang, Kai-Yuan Han, Zheng Zhang, Hui Ding, Li Liu, You-Yu Wang

{"title":"TF-loop: deciphering the transcription factor regulatory language for CTCF-mediated chromatin loop based on BERT.","authors":"Yi-Xuan Qi, Hao-Jiang Zhang, Hao-Xiang Tang, Zi-Xuan Zhang, Kai-Yuan Han, Zheng Zhang, Hui Ding, Li Liu, You-Yu Wang","doi":"10.1093/bib/bbag162","DOIUrl":"10.1093/bib/bbag162","url":null,"abstract":"Chromatin looping, which facilitates the three-dimensional (3D) organization of the genome, is essential for the regulation of gene expression. This process relies on the interaction of numerous transcription factors (TFs), particularly CCCTC-binding factor (CTCF) and Cohesin, whose dynamic binding patterns orchestrate loop formation. Current computational methods for prediction of CTCF-mediated chromatin loops struggle to perform genome-wide predictions, primarily due to the extreme imbalance between positive and negative samples in training datasets. Existing DNA-sequence-based models often fail to capture the complex dynamics of TF binding and the regulatory code behind chromatin looping. To address these challenges, we present TF-loop, a novel TF regulatory language framework designed to predict chromatin loops. This framework conceptualizes TF sequences, defined by the binding positions and orientations of five key TFs, as a structured \"TF language.\" Using the BERT model, TF-loop decodes the latent linguistic patterns embedded in these sequences, facilitating accurate predictions of chromatin loops. Comparative analysis with state-of-the-art model demonstrates that TF-loop significantly improves prediction accuracy across diverse cell types, even when faced with highly imbalanced datasets. The results highlight the potential of TF-loop to offer a new perspective on decoding the 3D structure of chromatin using natural language processing techniques.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 2","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13076942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670185","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}

引用次数: 0