BMC Bioinformatics最新文献

{"title":"Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.","authors":"Ibrahim Abdelbaky, Mohamed Elhakeem, Hilal Tayara, Elsayed Badr, Mustafa Abdul Salam","doi":"10.1186/s12859-024-05983-4","DOIUrl":"10.1186/s12859-024-05983-4","url":null,"abstract":"<p><p>Antimicrobial peptides (AMPs) are a promising class of antimicrobial drugs due to their broad-spectrum activity against microorganisms. However, their clinical application is limited by their potential to cause hemolysis, the destruction of red blood cells. To address this issue, we propose a deep learning model based on convolutional neural networks (CNNs) for predicting the hemolytic activity of AMPs. Peptide sequences are represented using one-hot encoding, and the CNN architecture consists of multiple convolutional and fully connected layers. The model was trained on six different datasets: HemoPI-1, HemoPI-2, HemoPI-3, RNN-Hem, Hlppredfuse, and AMP-Combined, achieving Matthew's correlation coefficients of 0.9274, 0.5614, 0.6051, 0.6142, 0.8799, and 0.7484, respectively. Our model outperforms previously reported methods and can facilitate the development of novel AMPs with reduced hemolytic activity, which is crucial for their therapeutic use in treating bacterial infections.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"368"},"PeriodicalIF":2.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11603801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738191","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}

{"title":"TreeWave: command line tool for alignment-free phylogeny reconstruction based on graphical representation of DNA sequences and genomic signal processing.","authors":"Nasma Boumajdi, Houda Bendani, Lahcen Belyamani, Azeddine Ibrahimi","doi":"10.1186/s12859-024-05992-3","DOIUrl":"10.1186/s12859-024-05992-3","url":null,"abstract":"<p><strong>Background: </strong>Genomic sequence similarity comparison is a crucial research area in bioinformatics. Multiple Sequence Alignment (MSA) is the basic technique used to identify regions of similarity between sequences, although MSA tools are widely used and highly accurate, they are often limited by computational complexity, and inaccuracies when handling highly divergent sequences, which leads to the development of alignment-free (AF) algorithms.</p><p><strong>Results: </strong>This paper presents TreeWave, a novel AF approach based on frequency chaos game representation and discrete wavelet transform of sequences for phylogeny inference. We validate our method on various genomic datasets such as complete virus genome sequences, bacteria genome sequences, human mitochondrial genome sequences, and rRNA gene sequences. Compared to classical methods, our tool demonstrates a significant reduction in running time, especially when analyzing large datasets. The resulting phylogenetic trees show that TreeWave has similar classification accuracy to the classical MSA methods based on the normalized Robinson-Foulds distances and Baker's Gamma coefficients.</p><p><strong>Conclusions: </strong>TreeWave is an open source and user-friendly command line tool for phylogeny reconstruction. It is a faster and more scalable tool that prioritizes computational efficiency while maintaining accuracy. TreeWave is freely available at https://github.com/nasmaB/TreeWave .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"367"},"PeriodicalIF":2.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738192","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}

{"title":"Human limits in machine learning: prediction of potato yield and disease using soil microbiome data.","authors":"Rosa Aghdam, Xudong Tang, Shan Shan, Richard Lankau, Claudia Solís-Lemus","doi":"10.1186/s12859-024-05977-2","DOIUrl":"10.1186/s12859-024-05977-2","url":null,"abstract":"<p><strong>Background: </strong>The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network.</p><p><strong>Results: </strong>Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can't classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power.</p><p><strong>Conclusions: </strong>Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"366"},"PeriodicalIF":2.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142725963","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}

{"title":"Plaseval: a framework for comparing and evaluating plasmid detection tools.","authors":"Aniket Mane, Haley Sanderson, Aaron P White, Rahat Zaheer, Robert Beiko, Cédric Chauve","doi":"10.1186/s12859-024-05941-0","DOIUrl":"10.1186/s12859-024-05941-0","url":null,"abstract":"<p><strong>Background: </strong>Plasmids play a major role in the transfer of antimicrobial resistance (AMR) genes among bacteria via horizontal gene transfer. The identification of plasmids in short-read assemblies is a challenging problem and a very active research area. Plasmid binning aims at detecting, in a draft genome assembly, groups (bins) of contigs likely to originate from the same plasmid. Several methods for plasmid binning have been developed recently, such as PlasBin-flow, HyAsP, gplas, MOB-suite, and plasmidSPAdes. This motivates the problem of evaluating the performances of plasmid binning methods, either against a given ground truth or between them.</p><p><strong>Results: </strong>We describe PlasEval, a novel method aimed at comparing the results of plasmid binning tools. PlasEval computes a dissimilarity measure between two sets of plasmid bins, that can originate either from two plasmid binning tools, or from a plasmid binning tool and a ground truth set of plasmid bins. The PlasEval dissimilarity accounts for the contig content of plasmid bins, the length of contigs and is repeat-aware. Moreover, the dissimilarity score computed by PlasEval is broken down into several parts, that allows to understand qualitative differences between the compared sets of plasmid bins. We illustrate the use of PlasEval by benchmarking four recently developed plasmid binning tools-PlasBin-flow, HyAsP, gplas, and MOB-recon-on a data set of 53 E. coli bacterial genomes.</p><p><strong>Conclusion: </strong>Analysis of the results of plasmid binning methods using PlasEval shows that their behaviour varies significantly. PlasEval can be used to decide which specific plasmid binning method should be used for a specific dataset. The disagreement between different methods also suggests that the problem of plasmid binning on short-read contigs requires further research. We believe that PlasEval can prove to be an effective tool in this regard. PlasEval is publicly available at https://github.com/acme92/PlasEval.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"365"},"PeriodicalIF":2.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142724618","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}

{"title":"MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification.","authors":"Lei Cheng, Qian Huang, Zhengqun Zhu, Yanan Li, Shuguang Ge, Longzhen Zhang, Ping Gong","doi":"10.1186/s12859-024-05989-y","DOIUrl":"10.1186/s12859-024-05989-y","url":null,"abstract":"<p><strong>Background: </strong>The integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies.</p><p><strong>Results: </strong>We propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier.</p><p><strong>Conclusions: </strong>Experimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"364"},"PeriodicalIF":2.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695114","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}

{"title":"PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3'-end sequencing.","authors":"Quinlan Furumo, Michelle M Meyer","doi":"10.1186/s12859-024-05982-5","DOIUrl":"10.1186/s12859-024-05982-5","url":null,"abstract":"<p><strong>Background: </strong>Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are growing amounts of bacterial 3'-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3' or 5' UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by different labs, using different organisms.</p><p><strong>Results: </strong>We present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two different datasets from two different organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3'-end positions not captured by PIPETS showed that they were uniformly very low coverage.</p><p><strong>Conclusions: </strong>PIPETS provides a broadly applicable platform to explore and analyze 3'-end sequencing data sets from across different organisms. It requires only the 3'-end sequencing data, and is broadly accessible to non-expert users.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"363"},"PeriodicalIF":2.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695120","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}

{"title":"ClassifieR 2.0: expanding interactive gene expression-based stratification to prostate and high-grade serous ovarian cancer.","authors":"Aideen McCabe, Gerard P Quinn, Suneil Jain, Micheál Ó Dálaigh, Kellie Dean, Ross G Murphy, Simon S McDade","doi":"10.1186/s12859-024-05981-6","DOIUrl":"10.1186/s12859-024-05981-6","url":null,"abstract":"<p><strong>Background: </strong>Advances in transcriptional profiling methods have enabled the discovery of molecular subtypes within and across traditional tissue-based cancer classifications. Such molecular subgroups hold potential for improving patient outcomes by guiding treatment decisions and revealing physiological distinctions and targetable pathways. Computational methods for stratifying transcriptomic data into molecular subgroups are increasingly abundant. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time-consuming and requires significant bioinformatics expertise. To address this need, we recently reported \"ClassifieR,\" a flexible, interactive cloud application for the functional annotation of colorectal and breast cancer transcriptomes. Here, we report \"ClassifieR 2.0\" which introduces additional modules for the molecular subtyping of prostate and high-grade serous ovarian cancer (HGSOC).</p><p><strong>Results: </strong>ClassifieR 2.0 introduces ClassifieRp and ClassifieRov, two specialised modules specifically designed to address the challenges of prostate and HGSOC molecular classification. ClassifieRp includes sigInfer, a method we developed to infer commercial prognostic prostate gene expression signatures from publicly available gene-lists or indeed any user-uploaded gene-list. ClassifieRov utilizes consensus molecular subtyping methods for HGSOC, including tools like consensusOV, for accurate ovarian cancer stratification. Both modules include functionalities present in the original ClassifieR framework for estimating cellular composition, predicting transcription factor (TF) activity and single sample gene set enrichment analysis (ssGSEA).</p><p><strong>Conclusions: </strong>ClassifieR 2.0 combines molecular subtyping of prostate cancer and HGSOC with commonly used sample annotation tools in a single, user-friendly platform, allowing scientists without bioinformatics training to explore prostate and HGSOC transcriptional data without the need for extensive bioinformatics knowledge or manual data handling to operate various packages. Our sigInfer method within ClassifieRp enables the inference of commercially available gene signatures for prostate cancer, while ClassifieRov incorporates consensus molecular subtyping for HGSOC. Overall, ClassifieR 2.0 aims to make molecular subtyping more accessible to the wider research community. This is crucial for increased understanding of the molecular heterogeneity of these cancers and developing personalised treatment strategies.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"362"},"PeriodicalIF":2.9,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11580654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685936","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}

{"title":"Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.","authors":"Sumaiya Noor, Afshan Naseem, Hamid Hussain Awan, Wasiq Aslam, Salman Khan, Salman A AlQahtani, Nijad Ahmad","doi":"10.1186/s12859-024-05978-1","DOIUrl":"10.1186/s12859-024-05978-1","url":null,"abstract":"<p><strong>Background: </strong>RNA 5-methyluridine (m5U) modifications play a crucial role in biological processes, making their accurate identification a key focus in computational biology. This paper introduces Deep-m5U, a robust predictor designed to enhance the prediction of m5U modifications. The proposed method, named Deep-m5U, utilizes a hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, a Shapley Additive exPlanations (SHAP) algorithm for discriminant feature selection, and a deep neural network (DNN) as the classifier.</p><p><strong>Results: </strong>The model was evaluated using two benchmark datasets, i.e., Full Transcript and Mature mRNA. Deep-m5U achieved overall accuracies of 91.47% and 95.86% for the Full Transcript and Mature mRNA datasets with 10-fold cross-validation, and for independent samples, the model attained 92.94% and 95.17% accuracy.</p><p><strong>Conclusion: </strong>Compared to existing models, Deep-m5U showed approximately 5.23% and 3.73% higher accuracy on the training data and 3.95% and 3.26% higher accuracy on independent samples for the Full Transcript and Mature mRNA datasets, respectively. The reliability and effectiveness of Deep-m5U make it a valuable tool for scientists and a potential asset in pharmaceutical design and research.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"360"},"PeriodicalIF":2.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675013","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}

{"title":"Drug-target interaction prediction by integrating heterogeneous information with mutual attention network.","authors":"Yuanyuan Zhang, Yingdong Wang, Chaoyong Wu, Lingmin Zhan, Aoyi Wang, Caiping Cheng, Jinzhong Zhao, Wuxia Zhang, Jianxin Chen, Peng Li","doi":"10.1186/s12859-024-05976-3","DOIUrl":"10.1186/s12859-024-05976-3","url":null,"abstract":"<p><strong>Background: </strong>Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction.</p><p><strong>Methods: </strong>Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction.</p><p><strong>Results: </strong>DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"361"},"PeriodicalIF":2.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675016","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}

{"title":"Non parametric differential network analysis: a tool for unveiling specific molecular signatures.","authors":"Pietro Hiram Guzzi, Arkaprava Roy, Marianna Milano, Pierangelo Veltri","doi":"10.1186/s12859-024-05969-2","DOIUrl":"10.1186/s12859-024-05969-2","url":null,"abstract":"<p><strong>Background: </strong>The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data.</p><p><strong>Results: </strong>Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes.</p><p><strong>Conclusions: </strong>Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"359"},"PeriodicalIF":2.9,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667371","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}