6 citations · 14 across the 5 of their papers we have counts for
3 papers · 1 filter
Learning to engineer protein flexibility
Petr Kouba, Joan Planas-Iglesias, Jiri Damborsky +3
Generative machine learning models are increasingly being used to design novel proteins for therapeutic and biotechnological applications. However, the current methods mostly focus…
DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology
Omar Abdelghani Attafi, Damiano Clementel, Konstantinos Kyritsis +17
Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML resea…
Revealing data leakage in protein interaction benchmarks
Anton Bushuiev, Roman Bushuiev, Jiri Sedlar +4
In recent years, there has been remarkable progress in machine learning for protein-protein interactions. However, prior work has predominantly focused on improving learning algori…