XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites
King Saud University · Purdue University West Lafayette · +4 more institutions
Abstract
Posttranslational modifications (PTMs) are essential for regulating protein localization and stability, significantly affecting gene expression, biological functions, and genome replication. Among these, sumoylation a PTM that attaches a chemical group to protein sequences-plays a critical role in protein function. Identifying sumoylation sites is particularly important due to their links to Parkinson's and Alzheimer's. This study introduces XGBoost-Sumo, a robust model to predict sumoylation sites by integrating protein structure and sequence data. The model utilizes a transformer-based attention mechanism to encode peptides and extract evolutionary features through the PsePSSM-DWT approach. By fusing word…
Citation impact
- FWCI
- 31.43
- Percentile
- 100%
- References
- 60
Authors
7Topics & keywords
- Discriminative model
- SUMO protein
- Computer science
- Ensemble forecasting
- Artificial intelligence
- Machine learning
- Chemistry
- Reduced inequalities