Inspector: A lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling.

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ID: 85836
2020
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Abstract
Lysine succinylation is an important type of protein post-translational modification and plays a key role in regulating protein function and structural changes. The mechanism and function of succinylation have not been clarified. The key to better understanding the precise mechanism and functional role of succinylation is the identification of lysine succinylation sites. However, conventional experimental methods for succinylation identification are often expensive, time-consuming, and labor-intensive. Therefore, the new development of computational approaches to effectively identify lysine succinylation sites from sequence data is much needed. In this study, we proposed a novel predictor for lysine succinylation identification, Inspector, which was developed by using the random forest algorithm combined with a variety of sequence-based feature-encoding schemes. Edited nearest-neighbor undersampling method and adaptive synthetic oversampling approach were employed to solve dataset imbalance, and a two-step feature-selection strategy was applied to optimize the feature set for training the accuracy of the prediction model. Empirical studies on performance comparison with existing tools showed that Inspector was able to achieve competitive predictive performance for distinguishing lysine succinylation sites.
Reference Key
zhu2020inspectoranalytical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhu, Yan;Jia, Cangzhi;Li, Fuyi;Song, Jiangning;
Journal analytical biochemistry
Year 2020
DOI
S0003-2697(19)31042-5
URL
Keywords
random forest adaptive synthetic oversampling nearest neighbor undersampling

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