23rd Human Proteome Organization World Congress

Representing gene function using the Gene Ontology

Keynote speaker

Keynote lecture

Neuro-symbolic AI reveals the functions of proteins in complex systems

Keynote Lecture

The prediction of functions of proteins is crucial for understanding biological mechanisms and has significant implications in medical diagnostics. My talk will trace our decade-long exploration in designing novel machine learning and deep learning techniques to predict protein functions, described by the Gene Ontology (GO). I will discuss the evolution of these methods, highlight significant breakthroughs that have enhanced our ability to predict protein functions accurately and efficiently. A particular focus will be on the integration of neuro-symbolic AI methods, which combine machine learning with logical reasoning. These methods not only enable the incorporation of biological background knowledge into our models but also allow for zero-shot predictions --- predicting functions for proteins without prior observed examples. I will demonstrate how these advancements have provided interpretable insights into protein function and have been used in identifying candidate genes for Mendelian diseases and enhancing variant prioritization for rare disease diagnosis. This approach not only broadens our understanding of protein functions in biotechnology, health, and disease, but also opens new avenues for protein design.

Scientific session 3
Computational approaches to protein functions

Appointment

Program

Scientific session 3Computational approaches to protein functions

Appointment

Program

Scientific session 3
Computational approaches to protein functions