#5795. PreCar_Deep?A deep learning framework for prediction of protein carbonylation sites based on Borderline-SMOTE strategy

July 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Spectroscopy;
Analytical Chemistry;
Computer Science Applications;
Software;
Process Chemistry and Technology;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №5795.1 Contract №5795.2 Contract №5795.3 Contract №5795.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Carbonylation is an irreversible post-translational modification of proteins and regulates various cellular physiological processes. Due to the limitations of experimental methods, it is necessary to predict carbonylation sites by computational methods. In this paper, a new prediction model of carbonylation, Precar_Deep, is proposed. First, six feature extraction methods are used to obtain the original feature space from the protein sequences. Then, the Group LASSO method is used to remove redundant information and the oversampling Borderline-SMOTE method is employed to balance the data to obtain a new feature space. Finally, the processed data is input into the deep learning framework constructed in this paper to predict the carbonylation sites, and the performance of the model is evaluated by using 10-fold cross-validation and independent test datasets.
Keywords:
Borderline-SMOTE; Carbonylation; Deep learning framework; Group LASSO; Multi-information fusion

Contacts :
0