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Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods

文献类型：期刊论文

作者Chen, Lei2; Wei, Lai2; Wang, Shaopeng3; Cai, Yu-Dong3; Zhang, Yu-Hang4; Huang, Tao4; Xu, Xianling1; ,
刊名COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
出版日期2018
卷号21期号:6页码:393-402
关键词Post-translational modification nitrated tyrosine extreme learning machine minimum redundancy maximum relevance incremental feature selection
ISSN号1386-2073
DOI10.2174/1386207321666180531091619
文献子类Article
英文摘要Background: Accurately recognizing nitrated tyrosine residues from protein sequences would pave a way for understanding the mechanism of nitration and the screening of the tyrosine residues in sequences. Results: In this study, we proposed a prediction model that used the extreme learning machine (ELM) algorithm as the prediction engine to identify nitrated tyrosine residues. To encode each tyrosine residue, a sliding window technique was adopted to extract a peptide segment for each tyrosine residue, from which a number of features were extracted. These features were analyzed by a popular feature selection method, Minimum Redundancy Maximum Relevance (mRMR) method, producing a feature list, in which all features were ranked in a rigorous way. Then, the Incremental Feature Selection (IFS) method was utilized to discover the optimal features, on which the optimal ELM-based prediction model was built. This model produced satisfactory results on the training dataset with a Matthews correlation coefficient of 0.757. The model was also evaluated by an independent test dataset that contained only positive samples, yielding a sensitivity of 0.938. Conclusion: Compared to other prediction models that use classic machine learning algorithms as prediction engines on the same datasets with their own optimal features, the optimal ELM-based prediction model produced much better results, indicating the superiority of the proposed model for the identification of nitrated tyrosine residues from protein sequences.
学科主题Biochemistry & Molecular Biology ; Chemistry ; Pharmacology & Pharmacy
WOS关键词MANGANESE SUPEROXIDE-DISMUTASE ; AMINO-ACID-COMPOSITION ; NITRIC-OXIDE ; MOLECULAR FRAGMENTS ; PLASMA-PROTEINS ; SITES ; CLASSIFICATION ; IDENTIFICATION ; DISORDER ; INFORMATION
语种英语
WOS记录号WOS:000450160800002
出版者BENTHAM SCIENCE PUBL LTD
版本出版稿
源URL[http://202.127.25.144/handle/331004/787]  
专题中国科学院上海生命科学研究院营养科学研究所
作者单位1.Guangdong AIB Polytech, Dept Comp Sci, Guangzhou 510507, Guangdong, Peoples R China,
2.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China;
3.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China;
4.Chinese Acad Sci, Inst Hlth Sci, Shanghai Inst Biol Sci, Shanghai 200025, Peoples R China;
推荐引用方式
GB/T 7714		 Chen, Lei,Wei, Lai,Wang, Shaopeng,et al. Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods[J]. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,2018,21(6):393-402.
APA Chen, Lei.,Wei, Lai.,Wang, Shaopeng.,Cai, Yu-Dong.,Zhang, Yu-Hang.,...&,.(2018).Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods.COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,21(6),393-402.
MLA Chen, Lei,et al."Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods".COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING 21.6(2018):393-402.

入库方式： OAI收割

来源：上海营养与健康研究所

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