IET Systems Biology最新文献_第2页

DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation DDANet：用于脑出血分割的深度扩张注意力网络。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-11-24 DOI: 10.1049/syb2.12103

Haiyan Liu, Yu Zeng, Hao Li, Fuxin Wang, Jianjun Chang, Huaping Guo, Jian Zhang

{"title":"DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation","authors":"Haiyan Liu, Yu Zeng, Hao Li, Fuxin Wang, Jianjun Chang, Huaping Guo, Jian Zhang","doi":"10.1049/syb2.12103","DOIUrl":"10.1049/syb2.12103","url":null,"abstract":"<p>Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages. The authors introduce DDANet, a novel haematoma segmentation model for brain CT images. Specifically, a dilated convolution pooling block is introduced in the intermediate layers of the encoder to enhance feature extraction capabilities of middle layers. Additionally, the authors incorporate a self-attention mechanism to capture global semantic information of high-level features to guide the extraction and processing of low-level features, thereby enhancing the model's understanding of the overall structure while maintaining details. DDANet also integrates residual networks, channel attention, and spatial attention mechanisms for joint optimisation, effectively mitigating the severe class imbalance problem and aiding the training process. Experiments show that DDANet outperforms current methods, achieving the Dice coefficient, Jaccard index, sensitivity, accuracy, and a specificity of 0.712, 0.601, 0.73, 0.997, and 0.998, respectively. The code is available at https://github.com/hpguo1982/DDANet.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"285-297"},"PeriodicalIF":1.9,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Human essential gene identification based on feature fusion and feature screening 基于特征融合和特征筛选的人类基本基因识别。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-11-22 DOI: 10.1049/syb2.12105

Zhao-Yue Zhang, Yue-Er Fan, Cheng-Bing Huang, Meng-Ze Du

{"title":"Human essential gene identification based on feature fusion and feature screening","authors":"Zhao-Yue Zhang, Yue-Er Fan, Cheng-Bing Huang, Meng-Ze Du","doi":"10.1049/syb2.12105","DOIUrl":"10.1049/syb2.12105","url":null,"abstract":"<p>Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K-spaced Nucleic Acid Pairs, and Z-curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single-feature-based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"227-237"},"PeriodicalIF":1.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Inference and analysis of cell-cell communication of non-myeloid circulating cells in late sepsis based on single-cell RNA-seq 基于单细胞 RNA-seq 对脓毒症晚期非骨髓循环细胞的细胞间通讯进行推断和分析。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-11-22 DOI: 10.1049/syb2.12109

Yanyan Tao, Miaomiao Li, Cheng Liu

引用次数: 0

siRNAEfficacyDB: An experimentally supported small interfering RNA efficacy database siRNAEfficacyDB: 经实验支持的小干扰 RNA 药效数据库。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-11-14 DOI: 10.1049/syb2.12102

Yang Zhang, Ting Yang, Yu Yang, Dongsheng Xu, Yucheng Hu, Shuo Zhang, Nanchao Luo, Lin Ning, Liping Ren

{"title":"siRNAEfficacyDB: An experimentally supported small interfering RNA efficacy database","authors":"Yang Zhang, Ting Yang, Yu Yang, Dongsheng Xu, Yucheng Hu, Shuo Zhang, Nanchao Luo, Lin Ning, Liping Ren","doi":"10.1049/syb2.12102","DOIUrl":"10.1049/syb2.12102","url":null,"abstract":"<p>Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post-transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data. This database contains 3544 siRNA records, covering 42 target genes and 5 cell lines. It provides detailed information on siRNA sequences, target genes, inhibition efficiencies, experimental techniques, cell lines, siRNA concentrations, and incubation times. siRNAEfficacyDB offers a user-friendly web interface that makes it easy to query, browse and analyse data, enabling efficient access to siRNA-related information. In summary, siRNAEfficacyDB provides a useful data foundation for siRNA drug design and optimisation, serving as a valuable resource for advancing computer-aided siRNA design research and nucleic acid drug development. siRNAEfficacyDB is freely available at https://cellknowledge.com.cn/siRNAEfficacy for non-commercial use.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"199-207"},"PeriodicalIF":1.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy Deep-GB：利用 CNN-BiLSTM 架构和增强型 PSSM（采用三分割策略）进行球状蛋白质预测的新型深度学习模型。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-11-08 DOI: 10.1049/syb2.12108

Sonia Zouari, Farman Ali, Atef Masmoudi, Sarah Abu Ghazalah, Wajdi Alghamdi, Faris A. Kateb, Nouf Ibrahim

{"title":"Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy","authors":"Sonia Zouari, Farman Ali, Atef Masmoudi, Sarah Abu Ghazalah, Wajdi Alghamdi, Faris A. Kateb, Nouf Ibrahim","doi":"10.1049/syb2.12108","DOIUrl":"10.1049/syb2.12108","url":null,"abstract":"<p>Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. To address the need for precise GP identification, this research introduces an innovative approach that employs a hybrid-based deep learning model called Deep-GP. We generated two datasets based on primary sequences and developed a novel feature descriptor called, Consensus Sequence-based Trisection-Position Specific Scoring Matrix (CST-PSSM). The model training phase involved the application of deep learning techniques, including the bidirectional long short-term memory network (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). The BiLSTM and CNN were hybridised for ensemble learning. The CST-PSSM-based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. This demonstrates the potential of harnessing deep learning for precise GB prediction as a robust tool to expedite research, streamline drug discovery, and unveil novel therapeutic targets.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"208-217"},"PeriodicalIF":1.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms 开发一种性能更强的机器学习模型，用于预测急诊室急性呼吸道症状患者的 COVID-19。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-10-29 DOI: 10.1049/syb2.12101

Maha Mesfer Alghamdi, Naael H. Alazwary, Waleed A. Alsowayan, Mohmmed Algamdi, Ahmed F. Alohali, Mustafa A. Yasawy, Abeer M. Alghamdi, Abdullah M. Alassaf, Mohammed R. Alshehri, Hussein A. Aljaziri, Nujoud H. Almoqati, Shatha S. Alghamdi, Norah A. Bin Magbel, Tareq A. AlMazeedi, Nashaat K. Neyazi, Mona M. Alghamdi, Mohammed N. Alazwary

{"title":"Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms","authors":"Maha Mesfer Alghamdi, Naael H. Alazwary, Waleed A. Alsowayan, Mohmmed Algamdi, Ahmed F. Alohali, Mustafa A. Yasawy, Abeer M. Alghamdi, Abdullah M. Alassaf, Mohammed R. Alshehri, Hussein A. Aljaziri, Nujoud H. Almoqati, Shatha S. Alghamdi, Norah A. Bin Magbel, Tareq A. AlMazeedi, Nashaat K. Neyazi, Mona M. Alghamdi, Mohammed N. Alazwary","doi":"10.1049/syb2.12101","DOIUrl":"10.1049/syb2.12101","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID-19 in patients with acute respiratory symptoms. Data from 915 patients in two hospitals in Saudi Arabia were used, categorized into four groups based on chronic lung conditions and COVID-19 status. Four supervised machine learning algorithms—Random Forest, Bagging classifier, Decision Tree, and Logistic Regression—were employed to predict COVID-19. Feature selection identified 12 key variables for prediction, including CXR abnormalities, smoking status, and WBC count. The Random Forest model showed the highest accuracy at 99.07%, followed by Decision Tree, Bagging classifier, and Logistic Regression. The study concluded that machine learning algorithms, particularly Random Forest, can effectively predict and classify COVID-19 cases, supporting the development of computer-assisted diagnostic tools in healthcare.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"298-317"},"PeriodicalIF":1.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Mechanism of action of “cistanche deserticola–Polygala” in treating Alzheimer's disease based on network pharmacology methods and molecular docking analysis 基于网络药理学方法和分子对接分析的 "肉苁蓉-保利加 "治疗阿尔茨海默病的作用机制。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-10-11 DOI: 10.1049/syb2.12100

Shaoqiang Wang, Yifan Wang

引用次数: 0

Comprehensive transcriptome and scRNA-seq analyses uncover the expression and underlying mechanism of SYNJ2 in papillary thyroid carcinoma 全面的转录组和 scRNA-seq 分析揭示了 SYNJ2 在甲状腺乳头状癌中的表达及其潜在机制。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-10-06 DOI: 10.1049/syb2.12099

Yuan-Ping Yang, Zhi-Guang Huang, Jia-Yuan Luo, Juan He, Lin Shi, Gang Chen, Si-Yuan Chen, Yu-Wen Deng, Yi-Jia Yang, Yi-Jun Tang, Yu-Yan Pang

{"title":"Comprehensive transcriptome and scRNA-seq analyses uncover the expression and underlying mechanism of SYNJ2 in papillary thyroid carcinoma","authors":"Yuan-Ping Yang, Zhi-Guang Huang, Jia-Yuan Luo, Juan He, Lin Shi, Gang Chen, Si-Yuan Chen, Yu-Wen Deng, Yi-Jia Yang, Yi-Jun Tang, Yu-Yan Pang","doi":"10.1049/syb2.12099","DOIUrl":"10.1049/syb2.12099","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Synaptojanin 2 (SYNJ2) has crucial role in various tumors, but its role in papillary thyroid carcinoma (PTC) remains unexplored. This study first detected SYNJ2 protein expression in PTC using immunohistochemistry method and further assessed SYNJ2 mRNA expression through mRNA chip and RNA sequencing data and its association with clinical characteristics. Additionally, KEGG, GSVA, and GSEA analyses were conducted to investigate potential biological functions, while single-cell RNA sequencing data were used to explore SYNJ2's underlying mechanisms in PTC. Meanwhile, immune infiltration status in different SYNJ2 expression groups were analyzed. Besides, we investigated the immune checkpoint gene expression and implemented drug sensitivity analysis. Results indicated that SYNJ2 is highly expressed in PTC (SMD = 0.66 [95% CI: 0.17–1.15]) and could distinguish between PTC and non-PTC tissues (AUC = 0.74 [0.70–0.78]). Furthermore, the study identified 134 intersecting genes of DEGs and CEGs, mainly enriched in the angiogenesis and epithelial-mesenchymal transition (EMT) pathways. Subsequent analysis showed the above pathways were activated in PTC epithelial cells. PTC patients with high SYNJ2 expression showed higher sensitivity to the six common drugs. Summarily, SYNJ2 may promote PTC progression through angiogenesis and EMT pathways. High SYNJ2 expression is associated with better response to immunotherapy and chemotherapy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 5","pages":"183-198"},"PeriodicalIF":1.9,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations iGATTLDA：基于图注意和转换器的整合模型，用于预测 lncRNA 与疾病的关联。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-09-22 DOI: 10.1049/syb2.12098

Biffon Manyura Momanyi, Sebu Aboma Temesgen, Tian-Yu Wang, Hui Gao, Ru Gao, Hua Tang, Li-Xia Tang

{"title":"iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations","authors":"Biffon Manyura Momanyi, Sebu Aboma Temesgen, Tian-Yu Wang, Hui Gao, Ru Gao, Hua Tang, Li-Xia Tang","doi":"10.1049/syb2.12098","DOIUrl":"10.1049/syb2.12098","url":null,"abstract":"<p>Long non-coding RNAs (lncRNAs) have emerged as significant contributors to the regulation of various biological processes, and their dysregulation has been linked to a variety of human disorders. Accurate prediction of potential correlations between lncRNAs and diseases is crucial for advancing disease diagnostics and treatment procedures. The authors introduced a novel computational method, iGATTLDA, for the prediction of lncRNA-disease associations. The model utilised lncRNA and disease similarity matrices, with known associations represented in an adjacency matrix. A heterogeneous network was constructed, dissecting lncRNAs and diseases as nodes and their associations as edges. The Graph Attention Network (GAT) is employed to process initial features and corresponding adjacency information. GAT identified significant neighbouring nodes in the network, capturing intricate relationships between lncRNAs and diseases, and generating new feature representations. Subsequently, the transformer captures global dependencies and interactions across the entire sequence of features produced by the GAT. Consequently, iGATTLDA successfully captures complex relationships and interactions that conventional approaches may overlook. In evaluating iGATTLDA, it attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95 and an area under the precision recall curve (AUPRC) of 0.96 with a two-layer multilayer perceptron (MLP) classifier. These results were notably higher compared to the majority of previously proposed models, further substantiating the model’s efficiency in predicting potential lncRNA-disease associations by incorporating both local and global interactions. The implementation details can be obtained from https://github.com/momanyibiffon/iGATTLDA.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 5","pages":"172-182"},"PeriodicalIF":1.9,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 tumour-associated macrophage-based signature for deciphering prognosis and immunotherapy response in prostate cancer 基于肿瘤相关巨噬细胞的特征，用于解读前列腺癌的预后和免疫疗法反应。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2024-08-13 DOI: 10.1049/syb2.12097

Jian Wang, Tao Guo, Yuanyuan Mi, Xiangyu Meng, Shuang Xu, Feng Dai, Chengwen Sun, Yi Huang, Jun Wang, Lijie Zhu, Jianquan Hou, Sheng Wu

{"title":"A tumour-associated macrophage-based signature for deciphering prognosis and immunotherapy response in prostate cancer","authors":"Jian Wang, Tao Guo, Yuanyuan Mi, Xiangyu Meng, Shuang Xu, Feng Dai, Chengwen Sun, Yi Huang, Jun Wang, Lijie Zhu, Jianquan Hou, Sheng Wu","doi":"10.1049/syb2.12097","DOIUrl":"10.1049/syb2.12097","url":null,"abstract":"<p>For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour-associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy-sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC-XENA, GEO and TISCH databases were extracted for analysis. Based on both single-cell datasets and bulk transcriptome profiles, a macrophage-related score (MRS) consisting of the 10-gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial-mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high-MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E-knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 5","pages":"155-171"},"PeriodicalIF":1.9,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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