ArticleCurrent genomics2025
ResUbiNet: A Novel Deep Learning Architecture for Ubiquitination Site Prediction.
Article in Current genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- iBitter-HF: A Method for Bitter Peptide Sequence Identification Based on Hybrid Feature Embedding.Foods (Basel, Switzerland) · 2026Article
- The METTL3/TRIM37 axis contributes to the progression of non-alcoholic fatty liver disease by promoting CAV1 degradation.Molecular and cellular biochemistry · 2026Article
- AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.BMC biology · 2026Article
- KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.BMC biology · 2026Article
- GraphChIAr: genome-wide super-resolution reconstruction of protein-mediated remote chromatin interactions by augmenting hi-C interaction maps with multiple ChIP-seq profiles.Briefings in bioinformatics · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
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Authors and funding
5 authors.
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Abstract
Introduction: Ubiquitination, a unique post-translational modification, plays a cardinal role in diverse cellular functions such as protein degradation, signal transduction, DNA repair, and regulation of cell cycle. Thus, accurate prediction of potential ubiquitination sites is an urgent requirement for exploring the ubiquitination mechanism as well as the disease pathogenesis associated with ubiquitination processes. Methods: This study introduces a novel deep learning architecture, ResUbiNet, which utilized a protein language model (ProtTrans), amino acid properties, and BLOSUM62 matrix for sequence embedding and multiple state-of-the-art architectural components, Results: The results of cross-validation and external tests showed that the ResUbiNet model achieved better prediction performances in comparison with the available hCKSAAP_UbSite, RUBI, MDCapsUbi, and MusiteDeep models. Conclusion: ResUbiNet's integration of advanced features and architectures significantly enhances prediction performance, aiding in understanding ubiquitination mechanisms and related diseases.
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