Generalizing biomedical relation classification with neural adversarial domain adaptation.

Area of Science:

• Biomedical informatics
• Machine learning
• Natural language processing

Background:

• Biomedical relation classification is crucial for understanding biological processes.
• Creating large annotated datasets for relation classification is expensive and time-consuming.
• Existing datasets often suffer from biases, limiting model generalization.

Purpose of the Study:

• To develop a novel adversarial learning algorithm for unsupervised domain adaptation in biomedical relation classification.
• To address the challenge of cross-corpora generalization for relation classification models.
• To improve biased classifiers using unlabeled data from target domains.

Main Methods:

• A novel adversarial learning algorithm for unsupervised domain adaptation.
• Learning domain-invariant features through an adversarial process.
• Utilizing recent advances in neural network (NN) methods.

Main Results:

• Demonstrated the ability to learn domain-invariant features in NNs for relation classification.
• Showcased successful re-purposing of classifiers trained for one interaction type (e.g., protein-protein) to others (e.g., drug-drug).
• Achieved up to 30% improvement in F1-score compared to non-domain adaptation methods and state-of-the-art adversarial methods.

Conclusions:

• The proposed adversarial learning method effectively addresses cross-corpora generalization challenges in biomedical relation classification.
• Unsupervised domain adaptation using adversarial learning can significantly improve model performance without labeled target data.
• The approach shows promise for adapting models to various biomedical interaction types and datasets.