BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.

ABSTRACT: Accurate prediction of protein and peptide functions from amino acid sequences is essential for understanding biological processes and advancing biomolecular engineering. Due to the limitations of experimental methods, computational approaches, particularly machine learning, have gained significant attention. However, many existing tools are task-specific and lack adaptability. Here, we propose a BERT-BiLSTM-Attention-TCN Protein Function Prediction Framework (BBATProt), a versatile framework for predicting protein and peptide functions. BBATProt leverages transfer learning with a pretrained bidirectional encoder representations from transformer model to capture high-dimensional features. The custom network integrates bidirectional long short-term memory and temporal convolutional network

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12599320 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature