Scientists have developed an Artificial Intelligence (AI)-powered tool scGen which will help to map and studying the cellular response to diseases and their treatment beyond experimentally available data.
According to the researchers, scGen is a generative deep learning model that leverages ideas from image, sequence and language processing and applies them to model the behaviour of a cell performed on a computer or via computer simulation.
Research team planning to improve scGen, make it a fully data-driven formulation, increasing its predictive power to enable the study of combinations of perturbations. Large-scale atlases of organs in a healthy state are soon going to be available, in particular, within the Human Cell Atlas. This is a significant step in understanding cells, tissues and organs in a healthy state in a better way and providing a reference while diagnosing, monitoring and treating diseases.
scGen is the first tool that predicts cellular response out of sample, accurately modelling cellular response to perturbations e.g. disease, compounds and genetic interventions, is a central goal of computational biology. This means that scGen, if trained on data that captures the effect of perturbations for a given system, is able to make reliable predictions for a different system.
Alex Wolf, Rsearcher from Technical University of Munich, Germany said that they are optimising scGen to answer more and more complex questions about diseases.