A Transferability-Based Method for Evaluating the Protein Representation Learning.

Area of Science:

• Computational biology
• Machine learning in bioinformatics

Background:

• Self-supervised learning with protein language models (PLMs) is effective for biological tasks like drug discovery.
• Current evaluation relies on specific benchmarks, raising concerns about efficiency and comprehensiveness.
• There's a trend towards large-scale multimodal and multitask PLMs.

Purpose of the Study:

• To introduce a novel quantitative method for evaluating the transferability of multi-task pre-trained protein representations.
• To address limitations in current empirical assessment methods for PLMs.
• To provide a more comprehensive and efficient evaluation tool for protein representation learning.

Main Methods:

• Developed a transferability-based quantitative approach.
• Quantified similarities in latent space distributions between pre-trained and fine-tuned protein features.
• Constructed diverse protein-specific pre-training tasks for validation.

Main Results:

• Demonstrated a robust correlation between calculated transferability scores and actual downstream task performance.
• Validated the method across multiple biological domains and heterogeneous tasks.
• Showcased the method's effectiveness in evaluating protein representation learning.

Conclusions:

• The proposed quantitative method serves as a comprehensive and efficient tool for evaluating protein representation learning.
• This approach can guide the development and selection of effective multi-task PLMs.
• It offers a more reliable assessment than traditional empirical evaluations on limited benchmarks.