iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier.
188D features
Chi2 analysis
SMOTE-Tomek
glutarylation
unbalanced data
Journal
Journal of proteome research
ISSN: 1535-3907
Titre abrégé: J Proteome Res
Pays: United States
ID NLM: 101128775
Informations de publication
Date de publication:
01 01 2021
01 01 2021
Historique:
pubmed:
23
10
2020
medline:
22
6
2021
entrez:
22
10
2020
Statut:
ppublish
Résumé
Lysine glutarylation is a newly reported post-translational modification (PTM) that plays significant roles in regulating metabolic and mitochondrial processes. Accurate identification of protein glutarylation is the primary task to better investigate molecular functions and various applications. Due to the common disadvantages of the time-consuming and expensive nature of traditional biological sequencing techniques as well as the explosive growth of protein data, building precise computational models to rapidly diagnose glutarylation is a popular and feasible solution. In this work, we proposed a novel AdaBoost-based predictor called iGlu_AdaBoost to distinguish glutarylation and non-glutarylation sequences. Here, the top 37 features were chosen from a total of 1768 combined features using Chi2 following incremental feature selection (IFS) to build the model, including 188D, the composition of
Identifiants
pubmed: 33090794
doi: 10.1021/acs.jproteome.0c00314
doi:
Substances chimiques
Proteins
0
Lysine
K3Z4F929H6
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM