Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model
Joint Authors
Zhang, Lu
Liu, Min
Qin, Xinyi
Liu, Guangzhong
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-11
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Succinylation is an important posttranslational modification of proteins, which plays a key role in protein conformation regulation and cellular function control.
Many studies have shown that succinylation modification on protein lysine residue is closely related to the occurrence of many diseases.
To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately.
In this study, we develop a new model, IFS-LightGBM (BO), which utilizes the incremental feature selection (IFS) method, the LightGBM feature selection method, the Bayesian optimization algorithm, and the LightGBM classifier, to predict succinylation sites in proteins.
Specifically, pseudo amino acid composition (PseAAC), position-specific scoring matrix (PSSM), disorder status, and Composition of k-spaced Amino Acid Pairs (CKSAAP) are firstly employed to extract feature information.
Then, utilizing the combination of the LightGBM feature selection method and the incremental feature selection (IFS) method selects the optimal feature subset for the LightGBM classifier.
Finally, to increase prediction accuracy and reduce the computation load, the Bayesian optimization algorithm is used to optimize the parameters of the LightGBM classifier.
The results reveal that the IFS-LightGBM (BO)-based prediction model performs better when it is evaluated by some common metrics, such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), and F-measure.
American Psychological Association (APA)
Zhang, Lu& Liu, Min& Qin, Xinyi& Liu, Guangzhong. 2020. Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1139639
Modern Language Association (MLA)
Zhang, Lu…[et al.]. Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1139639
American Medical Association (AMA)
Zhang, Lu& Liu, Min& Qin, Xinyi& Liu, Guangzhong. Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1139639
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139639