A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses
Zhou, Xinrui1; Yin, Rui1; Kwoh, Chee-Keong1; Zheng, Jie2
2018-12-31
Source PublicationBMC GENOMICS
ISSN1471-2164
Volume19
Status已发表
DOI10.1186/s12864-018-5282-9
AbstractBackgroundThe evolution of influenza A viruses leads to the antigenic changes. Serological diagnosis of the antigenicity is usually labor-intensive, time-consuming and not suitable for early-stage detection. Computational prediction of the antigenic relationship between emerging and old strains of influenza viruses using viral sequences can facilitate large-scale antigenic characterization, especially for those viruses requiring high biosafety facilities, such as H5 and H7 influenza A viruses. However, most computational models require carefully designed subtype-specific features, thereby being restricted to only one subtype.MethodsIn this paper, we propose a Context-FreeEncoding Scheme (CFreeEnS) for pairs of protein sequences, which encodes a protein sequence dataset into a numeric matrix and then feeds the matrix into a downstream machine learning model. CFreeEnS is not only free from subtype-specific selected features but also able to improve the accuracy of predicting the antigenicity of influenza. Since CFreeEnS is subtype-free, it is applicable to predicting the antigenicity of diverse influenza subtypes, hopefully saving the biologists from conducting serological assays for highly pathogenic strains.ResultsThe accuracy of prediction on each subtype tested (A/H1N1, A/H3N2, A/H5N1, A/H9N2) is over 85%, and can be as high as 91.5%. This outperforms existing methods that use carefully designed subtype-specific features. Furthermore, we tested the CFreeEnS on the combined dataset of the four subtypes. The accuracy reaches 84.6%, much higher than the best performance 75.1% reported by other subtype-free models, i.e. regional band-based model and residue-based model, for predicting the antigenicity of influenza. Also, we investigate the performance of CFreeEnS when the model is trained and tested on different subtypes (i.e. transfer learning). The prediction accuracy using CFreeEnS is 84.3% when the model is trained on the A/H1N1 dataset and tested on the A/H5N1, better than the 75.2% using a regional band-based model.ConclusionsThe CFreeEnS not only improves the prediction of antigenicity on datasets with only one subtype but also outperforms existing methods when tested on a combined dataset with four subtypes of influenza viruses.
KeywordEncoding scheme Influenza Antigenicity Classification
Indexed BySCI ; CPCI
Language英语
Funding ProjectRG21/15 Tier 1 grant, Ministry of Education, Singapore[2015-T1-001-169-11]
WOS Research AreaBiotechnology & Applied Microbiology ; Genetics & Heredity
WOS SubjectBiotechnology & Applied Microbiology ; Genetics & Heredity
WOS IDWOS:000454632500011
PublisherBMC
WOS KeywordVARIANTS
Original Document TypeArticle ; Proceedings Paper
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29880
Collection信息科学与技术学院_PI研究组_郑杰组
信息科学与技术学院
Corresponding AuthorKwoh, Chee-Keong; Zheng, Jie
Affiliation1.Nanyang Technol Univ, Sch Comp Sci & Engn, Nanyang Ave, Singapore 639798, Singapore
2.ShanghaiTech Univ, Sch Informat Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China
Corresponding Author AffilicationSchool of Information Science and Technology
Recommended Citation
GB/T 7714
Zhou, Xinrui,Yin, Rui,Kwoh, Chee-Keong,et al. A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses[J]. BMC GENOMICS,2018,19.
APA Zhou, Xinrui,Yin, Rui,Kwoh, Chee-Keong,&Zheng, Jie.(2018).A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses.BMC GENOMICS,19.
MLA Zhou, Xinrui,et al."A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses".BMC GENOMICS 19(2018).
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