positive-unlabeled-learning

This paper proposes PUe, a framework for biased positive-unlabeled learning that uses normalized propensity scores and normalized inverse probability weighting to handle selection bias, improving classification under non-uniform label distributions.

This paper introduces Evo-PU, a positive-unlabeled learning framework that models survivorship bias in protein sequence data by leveraging evolutionary mutation processes. The authors demonstrate that Evo-PU outperforms standard PU methods and protein language models in predicting protein functionality for influenza, RSV, and SARS-CoV-2.