ArticleBioData mining2025
XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites.
Article in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
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- Deep learning-based identification of N6-methyladenine sites via hybrid feature fusion and SHAP-driven feature selection.Scientific reports · 2026Article
- Enhancing security information and event management with W2V-BERT-based real-time threat detection.Scientific reports · 2026Article
- Optimized deep intelligent parameter efficient fine-tuning protein language model system for predicting dephosphorylation site.Journal of molecular modeling · 2026Article
- Predicting circRNA subcellular localization by fusing circRNA sequence and network information.Scientific reports · 2026Article
- AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides.Interdisciplinary sciences, computational life sciences · 2026Article
- BiToxNet: a deep learning framework integrating multimodal features for accurate identification of neurotoxic peptides and proteins.BMC biology · 2026Article
- Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction.Scientific reports · 2026Article
- AMP-CapsNet: a multi-view feature fusion approach for antimicrobial peptide prediction using capsule networks.Genomics & informatics · 2026Article
- AI-MDT: an automatic and intelligent multidisciplinary team consultations platform for lung cancer diagnosis.Journal of cancer research and clinical oncology · 2026Article
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- AHDSN: an attention-enabled hybrid deep sequential network for cancer survivability prediction from multi-omics data.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data.Scientific reports · 2025Article
- AIPred: comprehensive prediction and analysis of non-histone acetylation via protein language model and interpretable machine learning.BMC biology · 2025Article
- Integrating AI in security information and event management for real time cyber defense.Scientific reports · 2025Article
- Peripheral blood multimodal integration via cross-attention for cancer immune profiling.BMC cancer · 2025Article
- MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.Scientific reports · 2025Article
- Predict the degree of secondary structures of the encoding sequences in DNA storage by deep learning model.Scientific reports · 2025Article
- N6-methyladenine identification using deep learning and discriminative feature integration.BMC medical genomics · 2025Article
Corrections and comments
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
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 embeddings with evolutionary descriptors, it applies the SHapley Additive exPlanations (SHAP) algorithm for optimal feature selection and uses eXtreme Gradient Boosting (XGBoost) for classification. XGBoost-Sumo achieved an impressive accuracy of 99.68% on benchmark datasets using 10-fold cross-validation and 96.08% on independent samples. This marks a significant improvement, outperforming existing models by 10.31% on training data and 2.74% on independent tests. The model's reliability and high performance make it a valuable resource for researchers, with strong potential for applications in pharmaceutical development.
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