Data collected from transcriptomic and mutation profiling of a cancer defines the growth drivers of this cancer and can predict its course and its response to therapeutic approaches. Transcriptomic data is complex and must be normalized to multiple factors that influence quantification of various genes. Only artificial intelligence (AI) approaches can address and adjust for the various biological and artificial factors that influence RNA quantification. With proper training, AI is capable of using transcriptomic and mutation profiles to predict morphology, flow cytometry, FISH data and to provide precise information on the tested sample. However , there are technical and biological limitation for this powerful approach that need to be considered. We will discuss the potential and clinical applications and limitations of combining transcriptomic and mutation profiling with AI, provide insights into the current applications of such approach and its integration into today’s precision medicine.
