iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice.

Autor: Lv H; Center for Informational Biology at University of Electronic Science and Technology of China, China., Zhang Y; Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, China., Wang JS; Center for Informational Biology at University of Electronic Science and Technology of China, China., Yuan SS; Center for Informational Biology at University of Electronic Science and Technology of China, China., Sun ZJ; Center for Informational Biology at University of Electronic Science and Technology of China, China., Dao FY; Center for Informational Biology at University of Electronic Science and Technology of China, China., Guan ZX; Center for Informational Biology at University of Electronic Science and Technology of China, China., Lin H; Center for Informational Biology at University of Electronic Science and Technology of China, China., Deng KJ; Center for Informational Biology at University of Electronic Science and Technology of China, China.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2022 Jan 17; Vol. 23 (1).
DOI: 10.1093/bib/bbab486
Abstrakt: Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS.
(© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
Databáze: MEDLINE
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