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


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

Author(s): Lei Chen*, ShaoPeng Wang , Yu-Hang Zhang , Lai Wei , XianLing Xu, Tao Huang *, Yu-Dong Cai *.

Journal Name: Combinatorial Chemistry & High Throughput Screening

Volume 21 , Issue 6 , 2018

Abstract:

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.

Keywords: Post-translational modification, nitrated tyrosine, extreme learning machine, minimum redundancy maximum relevance, incremental feature selection.

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Article Details

VOLUME: 21
ISSUE: 6
Year: 2018
Page: [393 - 402]
Pages: 10
DOI: 10.2174/1386207321666180531091619
Price: $95

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