Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.

ABSTRACT: Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well as aiding the development of therapeutic strategies. Effector predictors utilizing pre-trained protein language models (PLMs) have shown sound performance by leveraging extensive evolutionary and sequential protein features. However, the accuracy and sensitivity of effector prediction remain challenging. Here, we introduce a model named Contrastive-learning of Language Embedding and Biological Features (CLEF) leveraging contrastive learning to integrate PLM representations with supplementary biological features. Biologically information is captured in learned contextualized embeddings to yield meaningful representations. With cross-modality bi

SUBMITTER: Peng Y 

PROVIDER: S-EPMC11791096 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature