SuccSPred: Succinylation Sites Prediction Using Fused Feature Representation and Ranking Method

Ge, Ruiquan; Luo, Yizhang; Feng, Guanwen; Jia, Gangyong; Zhang, Hua; Cai, Xu; Xu, Gang; Wang, Pu

doi:10.1007/978-3-030-91415-8_17

Cited by 1 publication

(2 citation statements)

References 37 publications

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“…To prove the effectiveness of our predictor named pSuc-FFSEA, We performed a 10-fold cross-validation using the same training set to objectively compare pSuc-FFSEA with the existing methods, which are IFS-LightGBM( Zhang et al, 2020 ) and SuccSPred ( Ge et al, 2021 ). IFS-LightGBM was constructed based on LightGBM classifier and the combination of the LightGBM feature selection method and the incremental feature selection method.…”

Section: Resultsmentioning

confidence: 99%

“…In 2020, IFS-LightGBM used a combination of the LightGBM feature selection method and the incremental feature felection (IFS) method to select the optimal subset of features that extracted multiple types of feature information ( Zhang et al, 2020 ). In 2021, Ge et al proposed a method named SuccSPred to predict succinylation sites by fusing feature, ranking method and parsimonious bayes to identify succinylation sites ( Ge et al, 2021 ). Clearly, considerable progress has been made in the prediction of lysine succinylation sites based on the traditional machine learning.…”

Section: Introductionmentioning

confidence: 99%

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pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm

Qiu

2022

Front. Cell Dev. Biol.

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Being a new type of widespread protein post-translational modifications discovered in recent years, succinylation plays a key role in protein conformational regulation and cellular function regulation. Numerous studies have shown that succinylation modifications are closely associated with the development of many diseases. In order to gain insight into the mechanism of succinylation, it is vital to identify lysine succinylation sites. However, experimental identification of succinylation sites is time-consuming and laborious, and traditional identification tools are unable to meet the rapid growth of datasets. Therefore, to solve this problem, we developed a new predictor named pSuc-FFSEA, which can predict succinylation sites in protein sequences by feature fusion and stacking ensemble algorithm. Specifically, the sequence information and physicochemical properties were first extracted using EBGW, One-Hot, continuous bag-of-words, chaos game representation, and AAF_DWT. Following that, feature selection was performed, which applied LASSO to select the optimal subset of features for the classifier, and then, stacking ensemble classifier was designed using two-layer stacking ensemble, selecting three classifiers, SVM, broad learning system and LightGBM classifier, as the base classifiers of the first layer, using logistic regression classifier as the meta classifier of the second layer. In order to further improve the model prediction accuracy and reduce the computational effort, bayesian optimization algorithm and grid search algorithm were utilized to optimize the hyperparameters of the classifier. Finally, the results of rigorous 10-fold cross-validation indicated our predictor showed excellent robustness and performed better than the previous prediction tools, which achieved an average prediction accuracy of 0.7773 ± 0.0120. Besides, for the convenience of the most experimental scientists, a user-friendly and comprehensive web-server for pSuc-FFSEA has been established at https://bio.cangmang.xyz/pSuc-FFSEA, by which one can easily obtain the expected data and results without going through the complicated mathematics.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%