Bioinformatics advances最新文献

DeepEPI: CNN-transformer-based model for extracting TF interactions through predicting enhancer-promoter interactions. DeepEPI：基于cnn -transformer的模型，通过预测增强子-启动子相互作用来提取TF相互作用。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-17 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf221

Seyedeh Fatemeh Tabatabaei, Saeedeh Akbari Roknabadi, Somayyeh Koohi

{"title":"DeepEPI: CNN-transformer-based model for extracting TF interactions through predicting enhancer-promoter interactions.","authors":"Seyedeh Fatemeh Tabatabaei, Saeedeh Akbari Roknabadi, Somayyeh Koohi","doi":"10.1093/bioadv/vbaf221","DOIUrl":"10.1093/bioadv/vbaf221","url":null,"abstract":"Motivation: We introduce DeepEPI, a deep learning framework for studying enhancer-promoter interactions (EPIs) directly from genomic sequences. By integrating convolutional neural networks (CNNs) with Transformer blocks, DeepEPI captures the complex regulatory interplay between enhancers and promoters, a key factor in gene expression and disease mechanisms. The model emphasizes interpretability and efficiency by employing embedding layers for OneHot encoding and multihead attention for detecting and analyzing transcription factor (TF) interactions. A DNA2Vec-based version of DeepEPI is also evaluated.Results: DeepEPI is assessed in two dimensions: comparison with existing models and analysis of encoding methods. Across six cell lines, DeepEPI consistently outperforms prior approaches. Compared to EPIVAN, it achieves a 2.4% gain in area under the precision-recall curve (AUPR) and maintains AUROC with DNA2Vec encoding, while with OneHot encoding it shows a 4% increase in AUPR and 1.9% in AUROC. Regarding encoding, DNA2Vec provides higher accuracy, but our OneHot-based embedding balances competitive performance with interpretability and reduced storage requirements. Beyond prediction, DeepEPI enhances biological insight by extracting meaningful TF-TF interactions from attention heads, effectively narrowing the search space for experimental validation. Validation analyses further support the biological relevance of these findings, underscoring DeepEPI's value for advancing EPI research.Availability and implementation: The source code of DeepEPI is available at: https://github.com/nazanintbtb/DeepEPI.git.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf221"},"PeriodicalIF":2.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Core fucose identification in glycoproteomics: an ML approach addressing fucose migration in mass spectrometry. 糖蛋白组学中的核心焦点鉴定：质谱中解决焦点迁移的ML方法。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-15 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf186

Yuanjie Su, Chang Jiang, Ziyue Yang, Shisheng Sun, Junying Zhang

{"title":"Core fucose identification in glycoproteomics: an ML approach addressing fucose migration in mass spectrometry.","authors":"Yuanjie Su, Chang Jiang, Ziyue Yang, Shisheng Sun, Junying Zhang","doi":"10.1093/bioadv/vbaf186","DOIUrl":"10.1093/bioadv/vbaf186","url":null,"abstract":"Motivation: Core fucosylation is a common type of glycosylation that plays a significant role in biological functions. Accurate identification of core fucosylated glycopeptides is challenging due to fucose migration phenomenon during mass spectrometry. By using glycopeptides from mouse brain with FUT8 knocked out as cases and core-fucosylated high-mannose glycans in normal mouse brain as controls, the phenomena are widely observed from mass spectrometry data. The relative intensities of 10 core-related characteristic ions are used jointly as a feature vector, and a semisupervised model and a self-supervised model are developed in the feature space with robustness of the models studied.Results: Experimental results show that both models perform well, with the former superior to the latter, reaching 99.95% identification accuracy on an independent mouse brain data with FUT8 knocked out. By applying the models to wild-type mouse brain, human IgG and human serum, their dominant abundance of core fucose and/or noncore fucose are found, which is trustworthy since the effect of fucose migration is dealt with. The study highlights the great significance of trustworthy data labeling, well-defined features, and machine learning/deep learning techniques in highly reliable, accurate, and robust identification of core fucose from high-throughput mass spectrometry data.Availability and implementation: The code for core fucose identification is freely available in https://github.com/yzy-010203/core_focuse_identification.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf186"},"PeriodicalIF":2.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MSA Class Logos: a web server for automated sequence logo generation for user-defined sequence classes based on one multiple sequence alignment. MSA类标识：一个web服务器自动序列标识生成自定义序列类基于一个多个序列对齐。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-11 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf217

Mauricio F González-Reyes, Fabio Durán-Verdugo, Alejandro Valdés-Jiménez, Janin Riedelsberger

{"title":"MSA Class Logos: a web server for automated sequence logo generation for user-defined sequence classes based on one multiple sequence alignment.","authors":"Mauricio F González-Reyes, Fabio Durán-Verdugo, Alejandro Valdés-Jiménez, Janin Riedelsberger","doi":"10.1093/bioadv/vbaf217","DOIUrl":"10.1093/bioadv/vbaf217","url":null,"abstract":"Summary: Sequence logos are a common way to visually represent amino acid frequencies and conserved sequence patterns of multiple sequence alignments (MSAs). MSA Class Logos is a free web server that enables users to browse and explore sequence logos of several sequence groups or classes simultaneously online, and to download amino acid frequencies for the entire MSA or the sequence classes. Amino acid frequencies can be downloaded in tabular or graphical form for further offline analysis. The core feature of MSA Class Logos is the user's ability to group amino acid sequences into classes or subgroups, generate sequence logos for each class, and compare the clearly arranged logos online. Sequence logos for the entire MSA and user-defined classes are generated in separate tabs and can be explored conveniently in parallel. Class-specific sequence logos facilitate the identification of class-specific conserved residues unique in evolutionary distant or functionally differing protein groups. Results can be explored visually directly on the web page or downloaded as tables in CSV format. Additionally, segments of each sequence logo can be downloaded in SVG format. MSA Class Logos is primarily intended for investigators without programming and scripting skills to ease detailed sequence analysis.Availability and implementation: MSA Class Logos and further documentation is available at https://msaclasslogos.appsbio.utalca.cl.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf217"},"PeriodicalIF":2.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12452264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PACO: a Shiny app for comparing perturbed pathways associated with different phenotypes. PACO：一个闪亮的应用程序，用于比较与不同表型相关的扰动通路。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-09 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf212

Giovanni Micale, Salvatore Alaimo, Alfredo Pulvirenti

{"title":"PACO: a Shiny app for comparing perturbed pathways associated with different phenotypes.","authors":"Giovanni Micale, Salvatore Alaimo, Alfredo Pulvirenti","doi":"10.1093/bioadv/vbaf212","DOIUrl":"10.1093/bioadv/vbaf212","url":null,"abstract":"Motivation: Pathways are biological networks describing interactions between genes, proteins, non-coding RNAs, drugs and chemical compounds that contribute to develop a specific metabolic function or biological process. Identifying perturbed pathways associated with a phenotype or condition helps to understand how functional processes are altered in complex diseases and which genes play a key role in these alterations. Recently, several algorithms have been developed to identify perturbed pathways associated with a phenotype. Still, no tools are available to visualize and compare perturbed pathways in the same species or different organisms.Results: Here, we present a web app called PAthway COmparator (PACO) to compare two or more sets of altered pathways associated with different phenotypes, starting from either custom data or simulation data returned by the pathway analysis algorithms MITHrIL and PHENSIM. The app allows users to visualize and compare the altered pathways, and zoom into specific regions. We show the potential applicability of PACO through a case study in which perturbed immune system pathways are compared in mice and humans after up-regulation of Interferon-stimulated gene 15 (ISG15).Availability and implementation: PACO is implemented as a Shiny R web application and is available at https://paco.dioncogen.eu/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf212"},"PeriodicalIF":2.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency. PerseuCPP：一种预测细胞穿透肽及其摄取效率的机器学习策略。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-08 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf213

Rayane Monique Bernardes-Loch, Gustavo de Oliveira Almeida, Igor Teixeira Brasiliano, Wagner Meira, Douglas E V Pires, Maria Cristina Baracat-Pereira, Sabrina de Azevedo Silveira

{"title":"PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency.","authors":"Rayane Monique Bernardes-Loch, Gustavo de Oliveira Almeida, Igor Teixeira Brasiliano, Wagner Meira, Douglas E V Pires, Maria Cristina Baracat-Pereira, Sabrina de Azevedo Silveira","doi":"10.1093/bioadv/vbaf213","DOIUrl":"10.1093/bioadv/vbaf213","url":null,"abstract":"Motivation: Cell-penetrating peptides (CPPs) are promising tools for transporting therapeutic molecules into cells without damaging the cellular membrane. These peptides serve as efficient drug delivery systems, capable of carrying diverse biologically active substances while exhibiting low cytotoxicity compared to non-native molecules. However, identifying CPPs through experimental methods is expensive and time-consuming, making computational strategies an attractive alternative due to their cost-effectiveness and scalability.Results: This study introduces PerseuCPP, a machine learning strategy designed to identify CPPs. Based on descriptors including physicochemical and structural properties as well as atomic composition, our strategy employs the Extremely Randomized Trees to predict CPPs and their uptake efficiency. The first stage was developed using a balanced dataset of 967 CPPs and non-CPPs, applying a 10-fold cross-validation scheme. Two independent datasets were utilized for validation. The CPP predictor achieved superior results compared to state-of-the-art methods, with MCC 0.854, Recall 0.860, and AUC 0.984. The second stage, focused on efficiency prediction, was trained on a balanced dataset of 140 CPPs and non-CPPs, also using a 10-fold cross-validation scheme, and validated with an independent dataset. The efficiency predictor achieved competitive results, with Recall 0.761 and AUC 0.690. PerseuCPP is interpretable, offering insights into the key descriptors enabling peptides to penetrate cells effectively. We anticipate that PerseuCPP will be a valuable tool for advancing the design and application of CPPs in drug delivery and biomedical research.Availability and implementation: https://github.com/goalmeida05/PERSEU.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf213"},"PeriodicalIF":2.8,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DRAGoN: a robust pipeline for analyzing DRUG-seq datasets. DRAGoN：用于分析DRUG-seq数据集的强大管道。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-08 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf214

Scott Norton, John M Gaspar

引用次数: 0

EMICSS: added-value annotations for EMDB entries. EMICSS: EMDB条目的附加值注释。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-05 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf203

Amudha Kumari Duraisamy, Neli Fonseca, Gerard J Kleywegt, Ardan Patwardhan, Kyle L Morris

{"title":"EMICSS: added-value annotations for EMDB entries.","authors":"Amudha Kumari Duraisamy, Neli Fonseca, Gerard J Kleywegt, Ardan Patwardhan, Kyle L Morris","doi":"10.1093/bioadv/vbaf203","DOIUrl":"10.1093/bioadv/vbaf203","url":null,"abstract":"Motivation: The electron microscopy data bank (EMDB) is a key repository for three-dimensional electron microscopy (3DEM) data but lacks comprehensive annotations and connections to many related biological, functional, and structural data resources. This limitation arises from the optional nature of such information to reduce depositor burden and the complexity of maintaining up-to-date external references, often requiring depositor consent. To address these challenges, we developed EMDB Integration with Complexes, Structures, and Sequences (EMICSS), an independent system that automatically updates cross-references with over 20 external resources, including UniProt, AlphaFold DB, PubMed, Complex Portal, and Gene Ontology.Results: EMICSS (https://www.ebi.ac.uk/emdb/emicss) annotations are accessible in multiple formats for every EMDB entry and its linked resources, and programmatically via the EMDB application programming interface. EMICSS plays a crucial role supporting the EMDB website, with annotations being used on entry pages, statistics, and in the search system.Availability and implementation: EMICSS is implemented in Python and it is an open-source, distributed under the Apache license version 2.0, with core code available on GitHub (https://github.com/emdb-empiar/added_annotations).","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf203"},"PeriodicalIF":2.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MADVAR: a lightweight, data-driven tool for automated feature selection in omics data. MADVAR：用于组学数据自动特征选择的轻量级数据驱动工具。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-04 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf211

Gilad Silberberg

{"title":"MADVAR: a lightweight, data-driven tool for automated feature selection in omics data.","authors":"Gilad Silberberg","doi":"10.1093/bioadv/vbaf211","DOIUrl":"10.1093/bioadv/vbaf211","url":null,"abstract":"Motivation: High-throughput biological data provides rich opportunities for discovery, but its vastness leads to the inclusion of many irrelevant features that hinder effective analysis, especially in unsupervised clustering and machine learning tasks. Traditional feature selection methods such as correlation filtering, PCA, mutual information, and Laplacian scores often either eliminate important features or demand extensive computational resources, and their thresholds are usually arbitrary rather than data-driven.Results: MADVAR addresses these challenges as a lightweight R package for automated feature selection in omics data, introducing two data-driven methods-madvar and intersectDistributions-that define thresholds based on the statistical structure of the data itself. These approaches eliminate the reliance on arbitrary cutoffs and efficiently filter features without expensive computation. Benchmarking across diverse omics datasets shows that MADVAR achieves top performance in clustering and classification tasks while maintaining computational efficiency, and it integrates seamlessly into existing R-based analysis pipelines.Availability and implementation: The source code and documentation for MADVAR are freely available on GitHub (https://github.com/Champions-Oncology/MADVAR). The package is implemented in R and runs on all major operating systems.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf211"},"PeriodicalIF":2.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

bloodAGENT: a versatile tool for blood group typing and genomic variation analysis. 血型分型和基因组变异分析的多功能工具。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-02 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf210

Michael Wittig, Tim A Steiert, Christoph Gassner, Andre Franke

{"title":"bloodAGENT: a versatile tool for blood group typing and genomic variation analysis.","authors":"Michael Wittig, Tim A Steiert, Christoph Gassner, Andre Franke","doi":"10.1093/bioadv/vbaf210","DOIUrl":"10.1093/bioadv/vbaf210","url":null,"abstract":"Motivation: Accurate blood group allele determination is essential for both research and clinical applications. While next-generation sequencing and third-generation sequencing technologies provide a wealth of genomic data, secondary analysis pipelines often struggle with detecting variations in paralogous regions and maintaining haplotype integrity. Existing tools for blood group allele determination are frequently proprietary and lack the flexibility needed to address these challenges. To bridge this gap, we developed bloodAGENT, a versatile and open-source tool designed for analyzing genomic variation and resolving blood group alleles.Results: bloodAGENT achieves high concordance in blood group allele determination under typical conditions but reveals a strong dependence on data completeness. Simulations show that dropout rates are the primary determinant of concordance and ambiguities, with noticeable declines at dropout rates as low as 5%. While phasing breaks have a minor overall impact, their importance remains evident in specific scenarios. These results highlight bloodAGENT's robustness and its potential for handling complex genomic analyses.Availability and implementation: https://github.com/ikmb/bloodAGENT.git.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf210"},"PeriodicalIF":2.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration of chaos game representation and integrative deep learning approaches for whole-genome sequencing-based grapevine genetic testing. 基于全基因组测序的葡萄基因检测中混沌博弈表征和综合深度学习方法的探索。

IF 2.8

Bioinformatics advances Pub Date : 2025-09-01 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf193

Andrew Vu, Brendan Park, Yifeng Li, Ping Liang

{"title":"Exploration of chaos game representation and integrative deep learning approaches for whole-genome sequencing-based grapevine genetic testing.","authors":"Andrew Vu, Brendan Park, Yifeng Li, Ping Liang","doi":"10.1093/bioadv/vbaf193","DOIUrl":"10.1093/bioadv/vbaf193","url":null,"abstract":"Motivation: The identification of grapevine species, cultivars, and clones associated with desired traits is an important component of viticulture. True-to-type identification is very challenging for grapevine due to the existence of a large number of cultivars and clones and the historical issues of synonyms and homonyms. DNA-based identification, superior to morphology-based methods, has been used as the current standard true-to-type method for grapevine, but not without shortcomings, such as the limited number of biomarkers and accessibility of services.Results: To overcome some of the limitations of traditional microsatellite-marker-based genetic testing, we explored a whole-genome-sequencing (WGS)-based approach to achieve the best accuracy at an affordable cost. To address the challenges of the extreme high dimensionality of the WGS data, we examined the effectiveness of using chaos game representation (CGR) to represent the genome sequence data and using deep learning for species and cultivar identification. CGR images provide a meaningful way to capture patterns for use with visual analysis, with the best results showing a 99% balanced accuracy in classifying five species, and a 80% balanced accuracy in predicting 41 cultivars. Our preliminary research highlights the potential for CGR and deep learning as a complementary tool for WGS-based species- and cultivar-level classification.Availability and implementation: Our implementation, including the pipeline for data processing and the four predictive models, is available at https://github.com/pliang64/CGR.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf193"},"PeriodicalIF":2.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0