ArticleiScience2025
Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- MIF-MAPMS: Enhancing identification of myelin autoantigenic peptides in multiple sclerosis through multimodal information fusion.Protein science : a publication of the Protein Society · 2026Article
- Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.Journal of computer-aided molecular design · 2026Article
- Quantitative structure-activity relationship characterization and modeling of length-varying bioactive peptides.Journal of computer-aided molecular design · 2026Article
- Artificial intelligence based approaches for prediction of antimicrobial resistance in ruminant host pathogens.World journal of microbiology & biotechnology · 2026Article
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- Enhancing feature selection for ordinal outcomes using resampling-based sparse linear discriminant analysis.Bioinformatics advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein succinylation is a vital post-translational modification that regulates diverse cellular processes. Accurate identification of succinylation sites is crucial for understanding protein function and development of targeted drugs. In this study, we propose an intelligent computational model, iSucc-SnCNs, which encodes protein sequences using the ProtGPT2-based protein language model. Structural representations are derived from SMR and PSSM matrices to extract SMR-HOG, SMR-DCT, and PSSM-DWT features. The BTGA+KNN algorithm selects top-ranked features from the hybrid feature vector. Finally, a self-normalized capsule neural network (Sn-CapsNet) is trained using a BTGA-based optimal feature set. The proposed iSucc-SnCNs achieved an accuracy of 92.92% and an AUC of 0.96, outperforming traditional models by 17%. The generalization of the iSucc-SnCNs model on two independent datasets (Ind-I and Ind-II) demonstrated improved performance by approximately 13% and 2%, respectively. These results highlight iSucc-SnCNs as a robust and efficient framework for large-scale succinylation site prediction and protein function analyses in drug discovery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.