IF 4.8 3区生物学

Methods Pub Date : 2024-05-21 DOI: 10.1016/j.ymeth.2024.05.015

Evgeny Nimerovsky , Daniel Sieme , Nasrollah Rezaei-Ghaleh

{"title":"Mobility of sodium ions in agarose gels probed through combined single- and triple-quantum NMR","authors":"Evgeny Nimerovsky , Daniel Sieme , Nasrollah Rezaei-Ghaleh","doi":"10.1016/j.ymeth.2024.05.015","DOIUrl":"10.1016/j.ymeth.2024.05.015","url":null,"abstract":"<div><p>Metal ions, including biologically prevalent sodium ions, can modulate electrostatic interactions frequently involved in the stability of condensed compartments in cells. Quantitative characterization of heterogeneous ion dynamics inside biomolecular condensates demands new experimental approaches. Here we develop a <sup>23</sup>Na NMR relaxation-based integrative approach to probe dynamics of sodium ions inside agarose gels as a model system. We exploit the electric quadrupole moment of spin-3/2 <sup>23</sup>Na nuclei and, through combination of single-quantum and triple-quantum-filtered <sup>23</sup>Na NMR relaxation methods, disentangle the relaxation contribution of different populations of sodium ions inside gels. Three populations of sodium ions are identified: a population with bi-exponential relaxation representing ions within the slow motion regime and two populations with mono-exponential relaxation but at different rates. Our study demonstrates the dynamical heterogeneity of sodium ions inside agarose gels and presents a new experimental approach for monitoring dynamics of sodium and other spin-3/2 ions (e.g. chloride) in condensed environments.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 55-64"},"PeriodicalIF":4.8,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001348/pdfft?md5=53d99b6b5eec66db96e7a8940a8829df&pid=1-s2.0-S1046202324001348-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141086378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules APEX-pHLA：准确预测外源短肽与 HLA I 类分子结合的新方法。

IF 4.8 3区生物学

Methods Pub Date : 2024-05-19 DOI: 10.1016/j.ymeth.2024.05.013

Zhihao Su , Yejian Wu , Kaiqiang Cao , Jie Du , Lujing Cao , Zhipeng Wu , Xinyi Wu , Xinqiao Wang , Ying Song , Xudong Wang , Hongliang Duan

{"title":"APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules","authors":"Zhihao Su , Yejian Wu , Kaiqiang Cao , Jie Du , Lujing Cao , Zhipeng Wu , Xinyi Wu , Xinqiao Wang , Ying Song , Xudong Wang , Hongliang Duan","doi":"10.1016/j.ymeth.2024.05.013","DOIUrl":"10.1016/j.ymeth.2024.05.013","url":null,"abstract":"<div><p>Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 38-47"},"PeriodicalIF":4.8,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches 利用基于统计矩的特征和集合方法预测植物非生物胁迫响应性 microRNA 的智能模型。

IF 4.8 3区生物学

Methods Pub Date : 2024-05-18 DOI: 10.1016/j.ymeth.2024.05.008

Ansar Naseem , Yaser Daanial Khan

{"title":"An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches","authors":"Ansar Naseem , Yaser Daanial Khan","doi":"10.1016/j.ymeth.2024.05.008","DOIUrl":"10.1016/j.ymeth.2024.05.008","url":null,"abstract":"<div><p>This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-silico prediction. Addressing this gap, the study seeks to develop an efficient computational model for plant stress response prediction. The two benchmark datasets for MiRNA and Pre-MiRNA dataset have been acquired in this study. Four ensemble approaches such as bagging, boosting, stacking, and blending have been employed. Classifiers such as Random Forest (RF), Extra Trees (ET), Ada Boost (ADB), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). Stacking and Blending employed all stated classifiers as base learners and Logistic Regression (LR) as Meta Classifier. There have been a total of four types of testing used, including independent set, self-consistency, cross-validation with 5 and 10 folds, and jackknife. This study has utilized evaluation metrics such as accuracy score, specificity, sensitivity, Mathew's correlation coefficient (MCC), and AUC. Our proposed methodology has outperformed existing state of the art study in both datasets based on independent set testing. The SVM-based approach has exhibited accuracy score of 0.659 for the MiRNA dataset, which is better than the previous study. The ET classifier has surpassed the accuracy of Pre-MiRNA dataset as compared to the existing benchmark study, achieving an impressive score of 0.67. The proposed method can be used in future research to predict abiotic stresses in plants.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 65-79"},"PeriodicalIF":4.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies 利用特征融合和优化策略计算预测 SARS-CoV-2 感染的磷酸化位点

IF 4.8 3区生物学

Methods Pub Date : 2024-05-18 DOI: 10.1016/j.ymeth.2024.04.021

Mumdooh J. Sabir , Majid Rasool Kamli , Ahmed Atef , Alawiah M. Alhibshi , Sherif Edris , Nahid H. Hajarah , Ahmed Bahieldin , Balachandran Manavalan , Jamal S.M. Sabir

{"title":"Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies","authors":"Mumdooh J. Sabir , Majid Rasool Kamli , Ahmed Atef , Alawiah M. Alhibshi , Sherif Edris , Nahid H. Hajarah , Ahmed Bahieldin , Balachandran Manavalan , Jamal S.M. Sabir","doi":"10.1016/j.ymeth.2024.04.021","DOIUrl":"10.1016/j.ymeth.2024.04.021","url":null,"abstract":"<div><p>SARS-CoV-2′s global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 1-8"},"PeriodicalIF":4.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data 从牛津纳米孔直接 RNA 测序数据中定量分析 N1-甲基腺苷 (m1A) RNA 甲基化。

IF 4.8 3区生物学

Methods Pub Date : 2024-05-18 DOI: 10.1016/j.ymeth.2024.05.009

Shenglun Chen , Jia Meng , Yuxin Zhang

{"title":"Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data","authors":"Shenglun Chen , Jia Meng , Yuxin Zhang","doi":"10.1016/j.ymeth.2024.05.009","DOIUrl":"10.1016/j.ymeth.2024.05.009","url":null,"abstract":"<div><p>With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 30-37"},"PeriodicalIF":4.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

OphthalMimic: A new alternative apparatus without animal tissue for the evaluation of topical ophthalmic drug products OphthalMimic：用于评估眼科局部用药产品的不含动物组织的新型替代仪器。

IF 4.8 3区生物学

Methods Pub Date : 2024-05-16 DOI: 10.1016/j.ymeth.2024.05.005

Geisa N. Barbalho , Manuel A. Falcão , Venâncio Alves Amaral , Jonad L.A. Contarato , Aliucha M. Barbalho , Gabriela Kaori Diógenes , Melyssa Mariana Gomes Silva , Beatriz Carvalho de Barros do Vale Rochelle , Guilherme M. Gelfuso , Marcilio Cunha-Filho , Tais Gratieri

{"title":"OphthalMimic: A new alternative apparatus without animal tissue for the evaluation of topical ophthalmic drug products","authors":"Geisa N. Barbalho , Manuel A. Falcão , Venâncio Alves Amaral , Jonad L.A. Contarato , Aliucha M. Barbalho , Gabriela Kaori Diógenes , Melyssa Mariana Gomes Silva , Beatriz Carvalho de Barros do Vale Rochelle , Guilherme M. Gelfuso , Marcilio Cunha-Filho , Tais Gratieri","doi":"10.1016/j.ymeth.2024.05.005","DOIUrl":"10.1016/j.ymeth.2024.05.005","url":null,"abstract":"<div><p>The necessity of animal-free performance tests for novel ophthalmic formulation screening is challenging. For this, we developed and validated a new device to simulate the dynamics and physical–chemical barriers of the eye for in vitro performance tests of topic ophthalmic formulations. The OphthalMimic is a 3D-printed device with an artificial lacrimal flow, a cul-de-sac area, a support base, and a simulated cornea comprised of a polymeric membrane containing poly-vinyl alcohol 10 % (w/v), gelatin 2.5 % (w/v), and different proportions of mucin and poloxamer, i.e., 1:1 (M1), 1:2 (M2), and 2:1 (M3) w/v, respectively. The support base is designed to move between 0° and 50° to replicate the movement of an eyelid. We challenged the model by testing the residence performance of poloxamer®407 16 % and poloxamer®407 16 % + chitosan 1 % (PLX16CS10) gels containing fluconazole. The test was conducted with a simulated tear flow of 1.0 mL.min<sup>−1</sup> for 5 min. The OphthalMimic successfully distinguished PLX16 and PLX16C10 formulations based on their fluconazole drainage (M1: 65 ± 14 % and 27 ± 10 %; M2: 58 ± 6 % and 38 ± 9 %; M3: 56 ± 5 % and 38 ± 18 %). In conclusion, the OphthalMimic is a promising tool for comparing the animal-free performance of ophthalmic formulations.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 1-11"},"PeriodicalIF":4.8,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

MGDDI: A multi-scale graph neural networks for drug–drug interaction prediction MGDDI：用于药物相互作用预测的多尺度图神经网络。

IF 4.8 3区生物学

Methods Pub Date : 2024-05-15 DOI: 10.1016/j.ymeth.2024.05.010

Guannan Geng , Lizhuang Wang , Yanwei Xu , Tianshuo Wang , Wei Ma , Hongliang Duan , Jiahui Zhang , Anqiong Mao

{"title":"MGDDI: A multi-scale graph neural networks for drug–drug interaction prediction","authors":"Guannan Geng , Lizhuang Wang , Yanwei Xu , Tianshuo Wang , Wei Ma , Hongliang Duan , Jiahui Zhang , Anqiong Mao","doi":"10.1016/j.ymeth.2024.05.010","DOIUrl":"10.1016/j.ymeth.2024.05.010","url":null,"abstract":"<div><p>Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 22-29"},"PeriodicalIF":4.8,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data 评估用于标注 scRNA-seq 数据的预训练语言模型的参数效率方法

IF 4.8 3区生物学

Methods Pub Date : 2024-05-15 DOI: 10.1016/j.ymeth.2024.05.007

Yucheng Xia , Yuhang Liu , Tianhao Li , Sihan He , Hong Chang , Yaqing Wang , Yongqing Zhang , Wenyi Ge

{"title":"Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data","authors":"Yucheng Xia , Yuhang Liu , Tianhao Li , Sihan He , Hong Chang , Yaqing Wang , Yongqing Zhang , Wenyi Ge","doi":"10.1016/j.ymeth.2024.05.007","DOIUrl":"10.1016/j.ymeth.2024.05.007","url":null,"abstract":"<div><p>Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 12-21"},"PeriodicalIF":4.8,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

PharmaRedefine: A database server for repurposing drugs against pathogenic bacteria PharmaRedefine：针对病原菌重新设计药物用途的数据库服务器

IF 4.8 3区生物学

Methods Pub Date : 2024-05-14 DOI: 10.1016/j.ymeth.2024.05.011

Longxiao Yuan, Jingjing Guo

引用次数: 0

N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites N-GlycoPred：用于准确识别 N-糖基化位点的混合深度学习模型。

IF 4.8 3区生物学

Methods Pub Date : 2024-05-10 DOI: 10.1016/j.ymeth.2024.05.002

Fengzhu Hu , Jie Gao , Jia Zheng , Cheekeong Kwoh , Cangzhi Jia

{"title":"N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites","authors":"Fengzhu Hu , Jie Gao , Jia Zheng , Cheekeong Kwoh , Cangzhi Jia","doi":"10.1016/j.ymeth.2024.05.002","DOIUrl":"10.1016/j.ymeth.2024.05.002","url":null,"abstract":"<div><p>Studies have shown that protein glycosylation in cells reflects the real-time dynamics of biological processes, and the occurrence and development of many diseases are closely related to protein glycosylation. Abnormal protein glycosylation can be used as a potential diagnostic and prognostic marker of a disease, as well as a therapeutic target and a new breakthrough point for exploring pathogenesis. To address the issue of significant differences in the prediction results of previous models for different species, we constructed a hybrid deep learning model N-GlycoPred on the basis of dual-layer convolution, a paired attention mechanism and BiLSTM for accurate identification of N-glycosylation sites. By adopting one-hot encoding or the AAindex, we specifically selected the optimum combination of features and deep learning frameworks for human and mouse to refine the models. Based on six independent test datasets, our N-GlycoPred model achieved an average AUC of 0.9553, which is 0.23% higher than MusiteDeep. The comparison results indicate that our model can serve as a powerful tool for N-glycosylation site prescreening for biological researchers.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 48-57"},"PeriodicalIF":4.8,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140907638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Methods最新文献