ArticleComputational and structural biotechnology journal2021
FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 49 citations in OpenAlex.
- Metabolic memory in the kidney: how lactate and lactylation drive the path from acute injury to chronic disease.Renal failure · 2026Review
- Training PBertKla on an Integrated Multi-Source Dataset with a Machine-Learning Layer for Lysine Lactylation Site Prediction.International journal of molecular sciences · 2026Article
- Metabolic Signaling Meets Epigenetic Regulation: How Protein Lactylation Remodels the Tumor Immune Microenvironment in Gastric Cancer.Current issues in molecular biology · 2026Review
- Lactate as a Chemical Modification on Proteins and Metabolites.Annual review of biochemistry · 2026Review
- Data Resources and Computational Methods for Lactylation Site Prediction: A Mini-Review.International journal of molecular sciences · 2026Review
- [Research progress on protein lactylation modification in malignant tumors].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Review
- Lactylation stabilizes PD-L1 to promote tumor immune evasion and cell growth.Cell death & disease · 2026Article
- The multifaceted functions of lactate and its translational potentials in cancer.Frontiers in pharmacology · 2026Review
- Unraveling the role of lactylation in cell death and correlative diseases: a comprehensive review.Frontiers in cell and developmental biology · 2026Review
- Protein lactylation in Alzheimer's disease: bridging metabolism, pathology, and therapeutic opportunity.Frontiers in aging neuroscience · 2026Review
- From metabolic substrate to epigenetic regulation: roles and mechanisms of lactylation in brain health and disease.Frontiers in molecular neuroscience · 2026Review
- PCBert-Kla: an efficient prediction method for lysine lactylation sites based on ProtBert and fusion of physicochemical features.Briefings in bioinformatics · 2025Article
- Research advances of lactylation modification in breast cancer.Discover oncology · 2025Review
- Short-chain acyl post-translational modifications in cancers: Mechanisms, roles, and therapeutic implications.Cancer communications (London, England) · 2025Review
- An efficient machine-learning framework for predicting protein post-translational modification sites.Scientific reports · 2025Article
- HybridKla: a hybrid deep learning framework for lactylation site prediction.Briefings in bioinformatics · 2025Article
- Mechanism and application of lactylation in cancers.Cell & bioscience · 2025Review
- Lactylation: From Homeostasis to Pathological Implications and Therapeutic Strategies.MedComm · 2025Review
- LDHA-mediated YAP lactylation promotes the tumor progression of hepatocellular carcinoma by inducing YAP dephosphorylation and activation.Biology direct · 2025Article
- PBertKla: a protein large language model for predicting human lysine lactylation sites.BMC biology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a novel lactate-derived post-translational modification (PTM), lysine lactylation (Kla) is involved in diverse biological processes, and participates in human tumorigenesis. Identification of Kla substrates with their exact sites is crucial for revealing the molecular mechanisms of lactylation. In contrast with labor-intensive and time-consuming experimental approaches, computational prediction of Kla could provide convenience and increased speed, but is still lacking. In this work, although current identified Kla sites are limited, we constructed the first Kla benchmark dataset and developed a few-shot learning-based architecture approach to leverage the power of small datasets and reduce the impact of imbalance and overfitting. A maximum 11.7% (0.745 versus 0.667) increase of area under the curve (AUC) value was achieved in contrast to conventional machine learning methods. We conducted a comprehensive survey of the performance by combining 8 sequence-based features and 3 structure-based features and tailored a multi-feature hybrid system for synergistic combination. This system achieved >16.2% improvement of the AUC value (0.889 versus 0.765) compared with single feature-based models for the prediction of Kla sites
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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 Socratic graph.