With the advent of high content screening methodologies (e.g. cellular imaging, transcriptomics, etc.), it becomes more challenging to tease apart and visualize batch effects. This is further compounded when building machine learning models which can easily use these confounding variables instead of real biological signal to generate predictions leading to poor real world relevance.
Gabe Muso, Chief Science Officer, BioSymetrics speaks with Rafael Rosengarten CEO of Genialis on the practice of improving data preprocessing for the advancement of machine learning in medicine.