Abstract
| Original language | English |
|---|---|
| Article number | bby079 |
| Number of pages | 15 |
| Journal | Briefings in Bioinformatics |
| DOIs | |
| Publication status | Accepted/In press - 2019 |
Keywords
- lysine malonylation
- computational prediction
- feature encoding methods
- machine learning
- ensemble learning
- light gradient boosting machine
Cite this
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Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. / Zhang, Yanju; Xie, Ruopeng; Wang, Jiawei ; Leier, Andre; Marquez-Lago, Tatiana T.; Akutsu, Tatsuya; Webb, Geoffrey I.; Chou, Kuo-Chen; Song, Jiangning.
In: Briefings in Bioinformatics, 2019.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework
AU - Zhang, Yanju
AU - Xie, Ruopeng
AU - Wang, Jiawei
AU - Leier, Andre
AU - Marquez-Lago, Tatiana T.
AU - Akutsu, Tatsuya
AU - Webb, Geoffrey I.
AU - Chou, Kuo-Chen
AU - Song, Jiangning
PY - 2019
Y1 - 2019
N2 - As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu.ezproxy.lib.monash.edu.au/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.
AB - As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu.ezproxy.lib.monash.edu.au/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.
KW - lysine malonylation
KW - computational prediction
KW - feature encoding methods
KW - machine learning
KW - ensemble learning
KW - light gradient boosting machine
U2 - 10.1093/bib/bby079
DO - 10.1093/bib/bby079
M3 - Article
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
SN - 1467-5463
M1 - bby079
ER -