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- W2969134071 abstract "Background: Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among.the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming. Objective: Herein, we propose a computational predictor named iMethylK-PseAAC to identify lysine methylation sites. Methods: Firstly, we constructed feature vectors based on PseAAC using position and composition relative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing. Results: The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing. Conclusion: It is concluded that iMethylK-PseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl-PseACC, BPB-PPMS and PMeS." @default.
- W2969134071 created "2019-08-22" @default.
- W2969134071 creator A5017084336 @default.
- W2969134071 creator A5038708054 @default.
- W2969134071 creator A5053106785 @default.
- W2969134071 creator A5071154222 @default.
- W2969134071 creator A5073505552 @default.
- W2969134071 creator A5075858344 @default.
- W2969134071 date "2019-10-14" @default.
- W2969134071 modified "2023-09-25" @default.
- W2969134071 title "iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule" @default.
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