ArticleBriefings in bioinformatics2025
HybridKla: a hybrid deep learning framework for lactylation site prediction.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- CircCDYL promotes glycolysis to drive the progression of nasopharyngeal carcinoma.Journal of advanced research · 2026Article
- Data Resources and Computational Methods for Lactylation Site Prediction: A Mini-Review.International journal of molecular sciences · 2026Review
- Protein lactylation in Alzheimer's disease: bridging metabolism, pathology, and therapeutic opportunity.Frontiers in aging neuroscience · 2026Review
- Beyond Fuel: Exercise-Induced Lactate as a Metabolic-Epigenetic Regulator in Central Nervous System Health and Disease.Biomolecules · 2025Review
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Authors and funding
6 authors.
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Abstract
Lysine lactylation (Kla), a novel lactate-derived post-translational modification, is involved in a myriad of biological processes and complex diseases. While several computational methods have been developed to identify Kla sites, these approaches still suffer from small datasets. In this work, we collected 23 984 Kla sites in 7297 proteins from the literature to construct the benchmark dataset. Leveraging recent advances in feature encoding, we tailored a multi-feature hybrid system, which integrated eight complementary feature-encoding strategies derived from two automated encoders and a composition-based module. Combining the hybrid system with deep learning, we presented our newly designed predictor named HybridKla, achieving an area under the curve (AUC) value of 0.8460. Compared to existing tools, HybridKla achieved >28.90% improvement of the AUC value (0.8460 versus 0.6563). we also conducted a proteome-wide search and provided a systematic prediction of Kla sites. The friendly online service of HybridKla is freely accessible for academic research at http://transkla.zzu.edu.cn/.
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Registered trials
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