InterLabelGO+

InterLabelGO+ integrates a deep learning-based method with an alignment-based method to improve protein function prediction by addressing label dependency and imbalance through a novel loss function and dynamic weighting.

Key Features:

• Hybrid Architecture: Combines deep learning-based and alignment-based methods for protein function prediction.
• Novel Loss Function: Addresses label dependency and imbalance in multi-label protein function prediction.
• Dynamic Weighting: Enhances performance through dynamic weighting of the alignment-based component.

Scientific Applications:

• Gene Ontology Term Prediction: Predicts Gene Ontology terms across various functional categories and evaluation metrics in large-scale protein function prediction tasks.
• Protein Function Annotation: Provides automated computational characterization of protein functions to address the gap between rapidly growing protein sequences and experimental characterization.

Methodology:

InterLabelGO+ integrates a deep learning-based method with an alignment-based method, incorporates a novel loss function that addresses label dependency and imbalance, and enhances performance through dynamic weighting of the alignment-based component.