BMC Bioinformatics最新文献

{"title":"DeBasher: a flow-based programming bash extension for the implementation of complex and interactive workflows with stateful processes.","authors":"Daniel Ortiz-Martínez","doi":"10.1186/s12859-025-06108-1","DOIUrl":"https://doi.org/10.1186/s12859-025-06108-1","url":null,"abstract":"<p><strong>Background: </strong>Bioinformatics data analysis faces significant challenges. As data analysis often takes the form of pipelines or workflows, workflow managers (WfMs) have become essential. Data flow programming constitutes the preferred approach in WfMs, enabling parallel processes activated reactively based on input availability. While this paradigm typically follows a linear, acyclic progression, cyclic workflows are sometimes necessary in bioinformatics analyses. These cyclic workflows also present an opportunity to explore workflow interactivity, a feature not widely implemented in existing WfMs.</p><p><strong>Results: </strong>We propose DeBasher, a tool that adopts the flow-based programming (FBP) paradigm, in which the workflow components are in control of their life cycle and can store state information, allowing the execution of complex workflows that include cycles. DeBasher also incorporates a powerful model of interactivity, where the user can alter the behavior of a running workflow. Additionally, DeBasher allows the user to define triggers so as to initiate the execution of a complete workflow or a part of it. The ability to execute processes with state and in control of their life cycle also has applications in dynamic scheduling tasks. Furthermore, DeBasher presents a series of extra features, including the combination of multiple workflows at runtime through a feature we have called runtime piping, switching to static scheduling to increase scalability, or implementing processes in multiple languages. DeBasher has been successfully used to process 131.7 TB of genomic data by means of a variant calling pipeline.</p><p><strong>Conclusions: </strong>DeBasher is an FBP Bash extension that can be useful in a wide range of situations and in particular when implementing complex workflows, workflows with interactivity or triggers, or when a high scalability is required.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"106"},"PeriodicalIF":2.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963140","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":"PRED-LD: efficient imputation of GWAS summary statistics.","authors":"Georgios A Manios, Aikaterini Michailidi, Panagiota I Kontou, Pantelis G Bagos","doi":"10.1186/s12859-025-06119-y","DOIUrl":"https://doi.org/10.1186/s12859-025-06119-y","url":null,"abstract":"<p><strong>Background: </strong>Genome-wide association studies have identified connections between genetic variations and diseases, but they only examine a small portion of single nucleotide polymorphisms. To enhance genetic findings, researchers suggest imputing genotypes for unmeasured SNPs to improve coverage and statistical power. When this is not possible, summary statistics imputation can be used as an alternative. The available summary statistics imputation tools rely on reference panels, such as the 1000 Genomes Project, to estimate linkage disequilibrium (LD) between variants for accurate imputation. Tools like FAPI and SSIMP use these reference panels in variant call format (VCF) for this purpose, though this process can be time-consuming. A more effective approach for processing reference panels in summary statistics imputation was proposed in RAISS. In this approach, the LD among the variants is precomputed from the reference panel, prior to imputation, thereby reducing computational time.</p><p><strong>Results: </strong>We present PRED-LD, an imputation method for GWAS summary statistics that aims to enhance the resolution of genetic association analyses. The proposed method uses precomputed linkage disequilibrium statistics from HapMap, Pheno Scanner and TOP-LD to impute summary statistics, given beta coefficients and standard errors. The single-point approach that we describe provides a fast and accurate way to estimate associations for untyped single nucleotide polymorphisms that exhibit high linkage disequilibrium (LD). The proposed method is faster, provides accurate imputation compared to existing tools, and has been implemented in both a web service ( https://compgen.dib.uth.gr/PRED-LD/ ) and a command-line tool ( https://github.com/pbagos/PRED-LD ), making it a useful resource for the research community.</p><p><strong>Conclusions: </strong>PRED-LD offers an efficient and accurate method for GWAS summary statistics imputation, providing faster performance, direct result interpretation, and the ability to use multiple reference panels. Also, the online version of PRED-LD simplifies obtaining LD information and performing imputation tasks without downloading reference panels and will be continuously updated to support tools for meta-analysis and fine-mapping in GWAS.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"107"},"PeriodicalIF":2.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963406","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":"Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis.","authors":"Camille Guilmineau, Marie Tremblay-Franco, Nathalie Vialaneix, Rémi Servien","doi":"10.1186/s12859-025-06118-z","DOIUrl":"https://doi.org/10.1186/s12859-025-06118-z","url":null,"abstract":"<p><strong>Background: </strong>Metabolomics describes the metabolic profile of an organism at a given time by the concentrations of its constituent metabolites. When studied over time, metabolite concentrations can help understand the dynamical evolution of a biological process. However, metabolites are involved into sequences of chemical reactions, called metabolic pathways, related to a given biological function. Accounting for these pathways into statistical methods for metabolomic data is thus a relevant way to directly express results in terms of biological functions and to increase their interpretability.</p><p><strong>Methods: </strong>We propose a new method, phoenics, to perform differential analysis for longitudinal metabolomic data at the pathway level. In short, phoenics proceeds in two steps: First, the matrix of metabolite quantifications is transformed by a dimension reduction approach accounting for pathway information. Then, a mixed linear model is fitted on the transformed data.</p><p><strong>Results: </strong>This method was applied to semi-synthetic NMR data and two real NMR datasets assessing the effects of antibiotics and irritable bowel syndrome on feces. Results showed that phoenics properly controls the Type I error rate and has a better ability to detect differential metabolic pathways and to extract new impacted biological functions than alternative methods. The method is implemented in the R package phoenics available on CRAN.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"105"},"PeriodicalIF":2.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961579","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":"Redefining the high variable genes by optimized LOESS regression with positive ratio.","authors":"Yue Xie, Zehua Jing, Hailin Pan, Xun Xu, Qi Fang","doi":"10.1186/s12859-025-06112-5","DOIUrl":"https://doi.org/10.1186/s12859-025-06112-5","url":null,"abstract":"<p><strong>Background: </strong>Single-cell RNA sequencing allows for the exploration of transcriptomic features at the individual cell level, but the high dimensionality and sparsity of the data pose substantial challenges for downstream analysis. Feature selection, therefore, is a critical step to reduce dimensionality and enhance interpretability.</p><p><strong>Results: </strong>We developed a robust feature selection algorithm that leverages optimized locally estimated scatterplot smoothing regression (LOESS) to precisely capture the relationship between gene average expression level and positive ratio while minimizing overfitting. Our evaluations showed that our algorithm consistently outperforms eight leading feature selection methods across three benchmark criteria and helps improve downstream analysis, thus offering a significant improvement in gene subset selection.</p><p><strong>Conclusions: </strong>By preserving key biological information through feature selection, GLP provides informative features to enhance the accuracy and effectiveness of downstream analyses.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"104"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143964553","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":"M01 tool: an automated, comprehensive computational tool for generating small molecule-peptide hybrids and docking them into curated protein structures.","authors":"Mahsa Sheikholeslami, Mohammad Hasan Nazari, Afshin Fassihi","doi":"10.1186/s12859-025-06120-5","DOIUrl":"https://doi.org/10.1186/s12859-025-06120-5","url":null,"abstract":"<p><strong>Background: </strong>The field of computational drug design is undergoing rapid advancements, highlighting the need for innovative methods to enhance the efficiency and accuracy of calculating ligand-receptor interactions. In this context, we introduce the M01 tool, a comprehensive computational package designed to facilitate the generation and docking of small molecule-peptide hybrids. M01 integrates several established tools, such as RDKit and EasyDock, into a user-friendly platform that automates the workflow from hybrid generation to docking simulations. This tool is particularly beneficial for researchers with limited chemistry expertise, helping them leverage advanced computational techniques.</p><p><strong>Results: </strong>The M01 tool features an intuitive interface for visualizing molecules and selecting connection points in generating new ligands. It also offers automated receptor preparation using UniProt or PDB IDs and generates default docking configuration files. Furthermore, it includes ligand preparation and docking capabilities through EasyDock and calculates molecular descriptors relevant to drug-likeness properties. Validation studies with peptide-alkoxyamine hybrids demonstrated the tool's effectiveness, generating over 14,000 unique hybrid molecules and showcasing its versatility in drug design applications.</p><p><strong>Conclusions: </strong>The M01 tool represents a significant advancement in computational drug design, streamlining the process of creating hybrid molecules and conducting docking studies. Its ability to automate complex workflows and provide essential molecular insights can empower researchers and enhance the development of novel therapeutics, ultimately contributing to more efficient drug discovery efforts.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"102"},"PeriodicalIF":2.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962551","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":"Improving data interpretability with new differential sample variance gene set tests.","authors":"Yasir Rahmatallah, Galina Glazko","doi":"10.1186/s12859-025-06117-0","DOIUrl":"https://doi.org/10.1186/s12859-025-06117-0","url":null,"abstract":"<p><strong>Background: </strong>Gene set analysis methods have played a major role in generating biological interpretations of omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression while methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.</p><p><strong>Results: </strong>We used ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterized the detection power and Type I error rate of these methods in addition to two methods developed earlier using simulation results with different parameters. We applied the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to within phenotype variability and to heterogeneous differences between two compared phenotypes. Our results show that methods designed to detect differential sample variance provide meaningful biological interpretations by detecting specific hallmark gene sets associated with the two compared phenotypes as documented in available literature.</p><p><strong>Conclusions: </strong>The results of this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"103"},"PeriodicalIF":2.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11998189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062127","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":"DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.","authors":"Daniel Henrique Ferreira Gomes, Inácio Gomes Medeiros, Tirzah Braz Petta, Beatriz Stransky, Jorge Estefano Santana de Souza","doi":"10.1186/s12859-025-06113-4","DOIUrl":"https://doi.org/10.1186/s12859-025-06113-4","url":null,"abstract":"<p><strong>Background: </strong>A significant challenge in precision medicine is confidently identifying mutations detected in sequencing processes that play roles in disease treatment or diagnosis. Furthermore, the lack of representativeness of single nucleotide variants in public databases and low sequencing rates in underrepresented populations pose defies, with many pathogenic mutations still awaiting discovery. Mutational pathogenicity predictors have gained relevance as supportive tools in medical decision-making. However, significant disagreement among different tools regarding pathogenicity identification is rooted, necessitating manual verification to confirm mutation effects accurately.</p><p><strong>Results: </strong>This article presents a cross-platform mobile application, DTreePred, an online visualization tool for assessing the pathogenicity of nucleotide variants. DTreePred utilizes a machine learning-based pathogenicity model, including a decision tree algorithm and 15 machine learning classifiers alongside classical predictors. Connecting public databases with diverse prediction algorithms streamlines variant analysis, whereas the decision tree algorithm enhances the accuracy and reliability of variant pathogenicity data. This integration of information from various sources and prediction techniques aims to serve as a functional guide for decision-making in clinical practice. In addition, we tested DTreePred in a case study involving a cohort from Rio Grande do Norte, Brazil. By categorizing nucleotide variants from the list of oncogenes and suppressor genes classified in ClinVar as inexact data, DTreePred successfully revealed the pathogenicity of more than 95% of the nucleotide variants. Furthermore, an integrity test with 200 known mutations yielded an accuracy of 97%, surpassing rates expected from previous models.</p><p><strong>Conclusions: </strong>DTreePred offers a robust solution for reducing uncertainty in clinical decision-making regarding pathogenic variants. Improving the accuracy of pathogenicity assessments has the potential to significantly increase the precision of medical diagnoses and treatments, particularly for underrepresented populations.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"101"},"PeriodicalIF":2.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965833","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":"Minimum uncertainty as Bayesian network model selection principle.","authors":"Grigoriy Gogoshin, Andrei S Rodin","doi":"10.1186/s12859-025-06104-5","DOIUrl":"10.1186/s12859-025-06104-5","url":null,"abstract":"<p><strong>Background: </strong>Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets frequently encountered in life science domains gives rise to contextual dependence and numerical irregularities in the behavior of model selection criteria (such as MDL, Minimum Description Length) used in BN reconstruction. This renders model features, first and foremost dependency strengths, incomparable and difficult to interpret. In this study, we derive and evaluate a model selection principle that addresses these problems.</p><p><strong>Results: </strong>The objective of the study is attained by (i) approaching model evaluation as a misspecification problem, (ii) estimating the effect that sampling error has on the satisfiability of conditional independence criterion, as reflected by Mutual Information, and (iii) utilizing this error estimate to penalize uncertainty with the novel Minimum Uncertainty (MU) model selection principle. We validate our findings numerically and demonstrate the performance advantages of the MU criterion. Finally, we illustrate the advantages of the new model evaluation framework on real data examples.</p><p><strong>Conclusions: </strong>The new BN model selection principle successfully overcomes performance irregularities observed with MDL, offers a superior average convergence rate in BN reconstruction, and improves the interpretability and universality of resulting BNs, thus enabling direct inter-BN comparisons and evaluations.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"100"},"PeriodicalIF":2.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11980298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810356","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":"DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.","authors":"Yuyao Yan, Xinyi Chai, Jiajun Liu, Sijia Wang, Wenran Li, Tao Huang","doi":"10.1186/s12859-025-06115-2","DOIUrl":"10.1186/s12859-025-06115-2","url":null,"abstract":"<p><p>Gene expression is the basis for cells to achieve various functions, while DNA methylation constitutes a critical epigenetic mechanism governing gene expression regulation. Here we propose DeepMethyGene, an adaptive recursive convolutional neural network model based on ResNet that predicts gene expression using DNA methylation information. Our model transforms methylation Beta values to M values for Gaussian distributed data optimization, dynamically adjusts the output channels according to input dimension, and implements residual blocks to mitigate the problem of gradient vanishing when training very deep networks. Benchmarking against the state-of-the-art geneEXPLORE model (R² = 0.449), DeepMethyGene (R² = 0.640) demonstrated superior predictive performance. Further analysis revealed that the number of methylation sites and the average distance between these sites and gene transcription start sites (TSS) significantly affected the prediction accuracy. By exploring the complex relationship between methylation and gene expression, this study provides theoretical support for disease progression prediction and clinical intervention. Relevant data and code are available at https://github.com/yaoyao-11/DeepMethyGene .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"99"},"PeriodicalIF":2.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810270","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":"Bayesian compositional generalized linear mixed models for disease prediction using microbiome data.","authors":"Li Zhang, Xinyan Zhang, Justin M Leach, A K M F Rahman, Carrie R Howell, Nengjun Yi","doi":"10.1186/s12859-025-06114-3","DOIUrl":"10.1186/s12859-025-06114-3","url":null,"abstract":"<p><p>The primary goal of predictive modeling for compositional microbiome data is to better understand and predict disease susceptibility based on the relative abundance of microbial species. Current approaches in this area often assume a high-dimensional sparse setting, where only a small subset of microbiome features is considered relevant to the outcome. However, in real-world data, both large and small effects frequently coexist, and acknowledging the contribution of smaller effects can significantly enhance predictive performance. To address this challenge, we developed Bayesian Compositional Generalized Linear Mixed Models for Analyzing Microbiome Data (BCGLMM). BCGLMM is capable of identifying both moderate taxa effects and the cumulative impact of numerous minor taxa, which are often overlooked in conventional models. With a sparsity-inducing prior, the structured regularized horseshoe prior, BCGLMM effectively collaborates phylogenetically related moderate effects. The random effect term efficiently captures sample-related minor effects by incorporating sample similarities within its variance-covariance matrix. We fitted the proposed models using Markov Chain Monte Carlo (MCMC) algorithms with rstan. The performance of the proposed method was evaluated through extensive simulation studies, demonstrating its superiority with higher prediction accuracy compared to existing methods. We then applied the proposed method on American Gut Data to predict inflammatory bowel disease (IBD). To ensure reproducibility, the code and data used in this paper are available at https://github.com/Li-Zhang28/BCGLMM .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"98"},"PeriodicalIF":2.9,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787694","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}