Genomic therapeutics, especially DNA and RNA editing, allow for the fixing of harmful mutations or adjusting the activity of cellular enzymes. While this technology has a great therapeutic potential, one of its main setbacks is its specificity and off-targets, i.e. how many and what unwanted mutations (which can potentially lead to autoimmunity and cancer) occur. We are developing a system for the quantification and the prediction of such off-target side effects using our computational models, applying big data approaches and AI. The methods we developed identify off-target activity with much higher fidelity compared with the commonly used methods, and therefore, can accelerate and significantly reduce the development cost of genomic therapeutics as common medicinal practice.
