Language model-based self-training reduces labeled data requirements by 99% for biological sequence classification.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Predicting biological sequence function is crucial for understanding disease mechanisms and genetic variation.
• Existing methods face challenges due to limited labeled data and complex sequence context modeling.
• Previous research has explored semi-supervised learning (SSL) and pre-trained language models (PLMs) separately, overlooking their combined potential.

Purpose of the Study:

• To develop an integrated framework combining PLMs and SSL for improved biological sequence function prediction.
• To leverage PLMs for feature extraction and SSL for decision boundary refinement.
• To demonstrate the framework's effectiveness in low-resource settings for tasks like DNA-binding protein (DBP) and non-coding RNA (ncRNA) detection.

Main Methods:

• Utilized PLMs as feature extractors to capture sequence semantics from large unlabeled datasets.
• Employed SSL with confidence-weighted pseudo-label selection to constrain the model's decision boundary.
• Applied the integrated framework to DNA-binding protein (DBP) and non-coding RNA (ncRNA) prediction tasks.

Main Results:

• Achieved competitive performance compared to fully supervised methods using significantly fewer labeled samples (as little as 1%).
• Demonstrated superior performance over traditional SSL methods like TSVM through a language model-based self-training approach.
• Successfully identified novel biomolecules, highlighting the framework's efficacy in low-resource scenarios.

Conclusions:

• The proposed framework offers an efficient solution for biological sequence classification, particularly in data-scarce environments.
• Integrating PLMs and SSL provides a powerful methodology for deciphering biological sequence function.
• This approach lays a foundation for advancing computational biology and discovering new biomolecules.