András Hatos

ButterflyVI integrates statistical learning and experimental data to annotate cancer variants and discover biomarkers, finding 60% of annotated variants were previously unknown.

The paper analyzes flanking regions of short linear motifs to identify amino acid attributes that may enhance binding specificity and strength.

3D-Beacons provides a federated network to unify access to protein structure data from multiple sources, enabling comparative analysis of models.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

APICURON credits biocurators by tracking their work across knowledgebases, using gamification and an API for resource integration.

The paper evaluates hydroxylation site predictors using independent datasets, finding they perform no better than random, highlighting issues with self-reported performance and generalization.