Biosciences use large sequencing projects to study current global challenges, like resilience to climate change, feeding a growing human population, combating emerging new pathogens etc. These sequencing projects generate massive amounts of sequence data that has to be annotated with biological functions, before it can be used. Due to its large size, this sequence data cannot be annotated manually. This has made Automated protein Function Prediction (AFP) research an important and active research topic. Our project will apply cutting edge Machine Learning to AFP and use innovative approaches to solve challenges presented by the paucity of high quality training data. One concrete result is an improved AFP method that is running in a web server freely accessible to bioscientists around the world. Our improved AFP method is expected have a global impact, just like our current methods, that are extensively used to tackle discussed global challenges.

2023

2026