FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction.

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 und

SUBMITTER: Jiang P 

PROVIDER: S-EPMC8385177 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature