Journal of Computational Biology最新文献_第3页

RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction. RCoxNet：一个整合随机行走、重新启动、突变和临床数据的深度学习框架，用于癌症生存预测。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-15 DOI: 10.1177/15578666261486475

Stuti Kumari, Sakshi Gujral, Abhishek Halder, Smruti Panda, Bernadette Mathew, Prashant Gupta, Ralf Herwig, Gaurav Ahuja, Debarka Sengupta

{"title":"RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction.","authors":"Stuti Kumari, Sakshi Gujral, Abhishek Halder, Smruti Panda, Bernadette Mathew, Prashant Gupta, Ralf Herwig, Gaurav Ahuja, Debarka Sengupta","doi":"10.1177/15578666261486475","DOIUrl":"https://doi.org/10.1177/15578666261486475","url":null,"abstract":"Accurate survival prediction in cancer remains challenging due to the sparsity of somatic mutation profiles and the failure of existing models to capture higher-order gene-gene dependencies. Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein-protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 ± 0.044 (BRCA), 0.750 ± 0.039 (LUNG), 0.704 ± 0.041 (GBM), and 0.668 ± 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261486475"},"PeriodicalIF":1.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148968999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Summarizing RNA Structural Ensembles via Maximum Agreement Secondary Structures. 基于最大一致二级结构的RNA结构集成综述。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-15 DOI: 10.1177/15578666261486480

Xinyu Gu, Stefan Ivanovic, Daniel W Feng, Mohammed El-Kebir

{"title":"Summarizing RNA Structural Ensembles via Maximum Agreement Secondary Structures.","authors":"Xinyu Gu, Stefan Ivanovic, Daniel W Feng, Mohammed El-Kebir","doi":"10.1177/15578666261486480","DOIUrl":"https://doi.org/10.1177/15578666261486480","url":null,"abstract":"Summarizing a collection <math><mstyle><mi>P</mi></mstyle></math> of related RNA secondary structures is a key challenge in applications like evolutionary analysis, alternative fold studies and mRNA vaccine design. This requires both clustering the input structures into similar groups and identifying the core structural motifs on which they agree or differ. Existing methods fail by focusing on only one of these goals: clustering methods do not output shared motifs, while consensus methods overlook the structural diversity present in the collection. Here, we introduce the Maximum Agreement Secondary Structures (MASS) problem, which seeks the largest set F of structural features present in <math><mstyle><mi>P</mi></mstyle></math> that partition the input structures into a user-specified number <math><mi>τ</mi></math> of distinct clusters. We prove that MASS is NP-hard and also establish its equivalence to a constrained binary matrix projection problem. We present an exact integer linear program, an exact combinatorial algorithm, and a scalable beam-search heuristic. Using simulations we demonstrate the performance of these exact algorithms and heuristics relative to baseline methods that focus on either clustering or identifying a single consensus tree. On real data, we demonstrate that MASS identifies conserved scaffolds in conformational datasets, reveals conserved structural motifs in different species within RNA families, and recovers shared structural features among synonymous transcripts encoding the same protein. MASS provides a general and interpretable framework for summarizing RNA structural organization.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261486480"},"PeriodicalIF":1.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148969004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Mapping-Profiles to Refine Strain-Level Metagenomic Classification. 利用图谱优化菌株水平宏基因组分类。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-12 DOI: 10.1177/15578666261485334

Josipa Lipovac, Lune Angevin, Krešimir KrižanoviC'

{"title":"Using Mapping-Profiles to Refine Strain-Level Metagenomic Classification.","authors":"Josipa Lipovac, Lune Angevin, Krešimir KrižanoviC'","doi":"10.1177/15578666261485334","DOIUrl":"https://doi.org/10.1177/15578666261485334","url":null,"abstract":"Metagenomic classification at the strain level remains challenging due to high sequence similarity among closely related genomes, which leads to ambiguous read mappings and frequent false-positive strain detections. Reducing such errors improves the reliability of strain-level analyses, which is critical for applications such as pathogen detection. We introduce StrainRefine, a post-mapping refinement method that analyzes read-reference mapping profiles to resolve ambiguous assignments among highly similar genomes. The method represents candidate reference genomes using binary profiles that capture read-support patterns and measures similarity between references based on profile overlap. The method clusters references based on similar mapping profiles, filters weakly supported genomes, and reassigns reads to representative references, reducing redundant reporting of near-identical strains. StrainRefine substantially reduces false-positive strain detections while preserving recall and improving agreement between predicted and true abundance profiles. On large-scale metagenomic datasets, it achieves a substantially improved precision-recall balance compared with existing mapping-based approaches, with the standalone method obtaining the highest read-level classification accuracy on the most complex evaluated dataset. Unlike many strain-level tools designed for individual species, StrainRefine operates without prior assumptions about sample composition or curated species-specific reference collections, while still achieving comparable performance in single-species settings on species-specific reference databases. These results highlight mapping-profile similarity as an effective signal for improving strain-level metagenomic classification.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261485334"},"PeriodicalIF":1.3,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148955910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Private Epigenetic Pacemaker Detector Using Homomorphic Encryption. 使用同态加密的私有表观遗传起搏器检测器。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-09 DOI: 10.1177/15578666261478652

Meir Goldenberg, Sagi Snir, Adi Akavia

引用次数: 0

PaNDA: Efficient Optimization of Phylogenetic Diversity in Networks. 网络中系统发育多样性的有效优化。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-08 DOI: 10.1177/15578666261481955

Niels Holtgrefe, Leo van Iersel, Ruben Meuwese, Yukihiro Murakami, Jannik Schestag

{"title":"PaNDA: Efficient Optimization of Phylogenetic Diversity in Networks.","authors":"Niels Holtgrefe, Leo van Iersel, Ruben Meuwese, Yukihiro Murakami, Jannik Schestag","doi":"10.1177/15578666261481955","DOIUrl":"https://doi.org/10.1177/15578666261481955","url":null,"abstract":"Phylogenetic diversity (PD) plays an important role in biodiversity, conservation, and evolutionary studies by measuring the diversity of a set of taxa based on their phylogenetic relationships. In phylogenetic trees, a subset of k taxa with maximum PD can be found by a simple and efficient greedy algorithm. However, this algorithmic tractability is lost when considering phylogenetic networks, which incorporate reticulate evolutionary events such as hybridization and horizontal gene transfer. To address this challenge, we introduce PaNDA (Phylogenetic Network Diversity Algorithms), the first software package and interactive graphical user-interface for exploring, visualizing, and maximizing diversity in phylogenetic networks. PaNDA includes a novel algorithm to find a subset of k taxa with maximum diversity, running in polynomial time for networks of bounded scanwidth, a measure of tree-likeness of a network that grows slower than the well-known level measure. This algorithm considers the variant of PD on networks in which the branch lengths of all paths from the root to the selected taxa contribute towards their diversity. We demonstrate the scalability of this algorithm on simulated networks, successfully analyzing level-15 networks with up to 200 taxa in seconds. We also provide a proof-of-concept analysis using a phylogenetic network on Xiphophorus species, illustrating how the tool can support diversity studies based on real genomic data. The software is easily installable and freely available at https://github.com/nholtgrefe/panda. Additionally, we extend the definition of PD to semi-directed phylogenetic networks, which are mixed graphs increasingly used in phylogenetic analysis to model uncertainty of the root location. We prove that finding a subset of k taxa with maximum diversity remains NP-hard on semi-directed networks, but do present a polynomial-time algorithm for networks with bounded level.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261481955"},"PeriodicalIF":1.3,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148911826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Supervised Prelabel Diffusion for Single-Cell Clustering. 单细胞聚类的动态监督预标记扩散。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-04 DOI: 10.1177/15578666261484978

Jiexia Tan, Chaoyu Li, Jinhu Peng

{"title":"Dynamic Supervised Prelabel Diffusion for Single-Cell Clustering.","authors":"Jiexia Tan, Chaoyu Li, Jinhu Peng","doi":"10.1177/15578666261484978","DOIUrl":"https://doi.org/10.1177/15578666261484978","url":null,"abstract":"Accurate identification of cell types from single-cell RNA sequencing data remains challenging due to high dimensionality, sparsity, and the limited availability of expert annotations. We propose a dynamic supervised prelabel diffusion framework that leverages a small set of verified cell-type labels to guide clustering through iterative representation learning. The framework couples a purity-controlled diffusion mechanism with a supervised contrastive objective, forming a self-reinforcing loop in which improved cell representations enable more accurate and adaptive prelabel propagation, which in turn enriches the supervisory signal for subsequent training. An adaptive Leiden clustering strategy automatically matches the target number of cell types, eliminating the need for manual resolution tuning. Experiments on five benchmark datasets show that the proposed method consistently outperforms both unsupervised and semi-supervised baselines in clustering accuracy, normalized mutual information, and adjusted Rand index, while achieving substantially lower computational cost. These results demonstrate the effectiveness of dynamic prelabel diffusion as a principled semi-supervised strategy for single-cell clustering under limited annotation budgets.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261484978"},"PeriodicalIF":1.3,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

scDFVA: Single-Cell Deep Clustering by Fusing Variational Graph Attention Autoencoder and ZINB-Based Autoencoder. scDFVA：融合变分图注意自编码器和基于zinb的自编码器的单细胞深度聚类。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-01 Epub Date: 2026-07-21 DOI: 10.1177/15578666261465432

Ge Zhang, Maohua Qin, Xuye Kou, Huimin Luo, Jianlin Wang, Junwei Luo

{"title":"scDFVA: Single-Cell Deep Clustering by Fusing Variational Graph Attention Autoencoder and ZINB-Based Autoencoder.","authors":"Ge Zhang, Maohua Qin, Xuye Kou, Huimin Luo, Jianlin Wang, Junwei Luo","doi":"10.1177/15578666261465432","DOIUrl":"10.1177/15578666261465432","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) provides a novel perspective to explore cellular biology at the single-cell resolution. Single-cell clustering is a crucial step to reveal cell types and the corresponding biological functions. However, when dealing with the high dimensionality and complexity of scRNA-seq data, existing deep models fail to comprehensively capture the intrinsic attribute information and structural relationships within the data. In this study, we propose a novel single-cell deep clustering model named scDFVA. The proposed scDFVA consists of a variational graph attention autoencoder (AE), a zero-inflated negative binomial (ZINB) based AE, and a self-supervised clustering. To better simulate sparse and zero-inflated scRNA-seq data, we incorporate the ZINB model into the AE. The variational graph attention AE is introduced to learn the cell structure information. scDFVA achieves representation learning within a joint framework comprising a ZINB-based AE and a variational graph attention AE, effectively fusing gene expression and cell structure information. Furthermore, scDFVA performs self-supervised clustering training on the latent fusion representations of cells to achieve mutual supervision between representation learning and clustering. Experiments indicated that scDFVA outperformed several other competing methods, demonstrating that our method is beneficial in single-cell clustering.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"913-928"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148535831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning. 利用无监督结构域适应和半监督学习进行单细胞RNA测序的细胞类型预测。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-01 Epub Date: 2026-06-10 DOI: 10.1177/15578666261457147

Chaelin Park, Joung Min Choi, Heejoon Chae

{"title":"Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.","authors":"Chaelin Park, Joung Min Choi, Heejoon Chae","doi":"10.1177/15578666261457147","DOIUrl":"10.1177/15578666261457147","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) techniques for measuring gene expression in individual cells have developed rapidly. Recently, the identification of cell types in scRNA-seq analysis has been accomplished using deep learning. Most methods utilize a dataset containing cell-type labels to train the model and then apply this model to other datasets. However, the integration of multiple datasets leads to unexpected batch effects caused by differences in laboratories, experimenters, and sequencing techniques. As the batch effect interrupts the biological signal of interest, an effective batch correction method is essential. In this article, we present scUDAS, a cell-type prediction model for scRNA-seq that utilizes unsupervised domain adaptation and semi-supervised learning (SSL) to reduce the differences in distributions between datasets. First, we pretrain the proposed model based on the source dataset, which contained cell-type information. Subsequently, scUDAS is trained on the target dataset by leveraging adversarial training to align the distribution of the target dataset with that of the source dataset. Finally, scUDAS was retrained to improve its performance through SSL by leveraging both the source and target datasets with consistency regularization. scUDAS outperformed the other deep learning-based batch correction models by appropriately removing the batch effect. scUDAS is publicly available at https://github.com/cbi-bioinfo/scUDAS.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"855-870"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148211411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment. 基于图匹配和点云配准的空间转录组学数据比对方法。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-01 Epub Date: 2026-06-27 DOI: 10.1177/15578666261462952

Longfei Tang, Shutong Xiao, Zhao He, Lu Ba, Yashu Xu, Yuhui Feng, Yiyuan Guo, Xiaoran Shi, Jing Qi

{"title":"GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.","authors":"Longfei Tang, Shutong Xiao, Zhao He, Lu Ba, Yashu Xu, Yuhui Feng, Yiyuan Guo, Xiaoran Shi, Jing Qi","doi":"10.1177/15578666261462952","DOIUrl":"10.1177/15578666261462952","url":null,"abstract":"Spatial transcriptomics (ST) often requires aligning multiple tissue slices to reconstruct three-dimensional biological structures, a task hindered by complex deformations and structural heterogeneity. We propose GMSA, a synergistic alignment framework that integrates graph matching with point cloud registration. Unlike existing tools, GMSA first identifies high-confidence correspondences through subgraph matching based on gene expression and spatial topology, which provides a robust initialization for subsequent rigid Iterative Closest Point (ICP) or Nonrigid Iterative Closest Point (NICP) registration. Benchmark results on dorsolateral prefrontal cortex (DLPFC), spatially-resolved transcript amplicon readout mapping (STARmap), and multiplexed error-robust fluorescence in situ hybridization (MERFISH) datasets demonstrate that GMSA consistently outperforms state-of-the-art methods in alignment accuracy. Notably, GMSA's nonrigid strategy successfully resolves complex structural distortions in MERFISH data where traditional methods fail, while maintaining stable gene expression distributions across aligned slices. This framework provides a flexible and precise solution for multimodal spatial transcriptomics integration.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"871-887"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148338663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods. 基于人工智能和图像处理方法的蚊子种类和性别识别系统。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-09-01 Epub Date: 2026-07-06 DOI: 10.1177/15578666261463268

Fu-Hsing Wu, Chuen-Horng Lin, Xin-Yi Zhang, Ching-Tung Peng, Kuo-Cheng Tseng, Chuan-Kang Liu, Shao-Wei Chu, Yu-Yi Chen, Wu-Chun Tu, Yung-Kuan Chan

{"title":"Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods.","authors":"Fu-Hsing Wu, Chuen-Horng Lin, Xin-Yi Zhang, Ching-Tung Peng, Kuo-Cheng Tseng, Chuan-Kang Liu, Shao-Wei Chu, Yu-Yi Chen, Wu-Chun Tu, Yung-Kuan Chan","doi":"10.1177/15578666261463268","DOIUrl":"10.1177/15578666261463268","url":null,"abstract":"Vector mosquito bites can significantly impact quality of life, pose health risks, and even lead to death. Different mosquito species can transmit various diseases, and their blood-sucking behavior varies by sex. Therefore, accurately identifying mosquito species and gender in a given area is crucial for epidemic prevention. This study aimed to develop an image processing and artificial intelligence (AI) system to accurately identify mosquito species and gender using mosquito images. An image dataset consisting of 12,552, 17,152, and 9853 images, captured against white, yellow sticky, and blue sticky paper backgrounds, respectively, and covering eight mosquito species, was employed to develop an advanced identification system. This system integrates image processing methods, YOLO-V3 models for segmenting individual mosquito images, and Inception-V4 models for identifying mosquito species and determining their gender. The proposed models achieved impressive accuracy rates of 0.9806, 0.9888, and 0.9899 for species identification, and 0.8741, 0.9173, and 0.9241 for gender identification, corresponding to the white, yellow sticky, and blue sticky paper backgrounds, respectively. Overall, our system demonstrates a high level of accuracy in identifying both mosquito species and gender.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"888-912"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0