ArticleBioMed research international2021
LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- UniPTMs: a unified multi-type PTM site prediction model via master-slave architecture-based multi-stage fusion strategy and hierarchical contrastive loss.BMC bioinformatics · 2026Article
- The Use of DeepQSAR Models for the Discovery of Peptides With Enhanced Antimicrobial and Antibiofilm Potential.Molecular informatics · 2026Article
- UniGraphPTMs: a contrastive learning-enhanced universal framework for PTM site prediction via GNN and multimodal fusion.BMC genomics · 2026Article
- Deep learning-driven TCRβ repertoire analysis enhances diagnosis and enables mining of immunological biomarkers in systemic lupus erythematosus.BioData mining · 2025Article
- RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores.Journal of cheminformatics · 2025Article
- MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.Briefings in bioinformatics · 2025Article
- KD_MultiSucc: incorporating multi-teacher knowledge distillation and word embeddings for cross-species prediction of protein succinylation sites.Biology methods & protocols · 2025Article
- PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites.PLoS computational biology · 2024Article
- Impact of Lysine Succinylation on the Biology of Fungi.Current issues in molecular biology · 2024Review
- RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences.Briefings in bioinformatics · 2023Article
- Protein post-translational modification by lysine succinylation: Biochemistry, biological implications, and therapeutic opportunities.Genes & diseases · 2023Review
- MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction.BMC bioinformatics · 2023Article
- pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module.BMC bioinformatics · 2022Article
- MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides.Pharmaceuticals (Basel, Switzerland) · 2022Article
- A capsule network-based method for identifying transcription factors.Frontiers in microbiology · 2022Article
- Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.Frontiers in genetics · 2022Article
- Mini-review: Recent advances in post-translational modification site prediction based on deep learning.Computational and structural biotechnology journal · 2022Review
- pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm.Frontiers in cell and developmental biology · 2022Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods were developed to deal with this challenge, few considered semantic relationship between residues. We combined long short-term memory (LSTM) and convolutional neural network (CNN) into a deep learning method for predicting succinylation site. The proposed method obtained a Matthews correlation coefficient of 0.2508 on the independent test, outperforming state of the art methods. We also performed the enrichment analysis of succinylation proteins. The results showed that functions of succinylation were conserved across species but differed to a certain extent with species. On basis of the proposed method, we developed a user-friendly web server for predicting succinylation sites.
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