IMO here ML actually delivers the biggest benefit today: by developing a machine learning classifier based on morphological features rather than molecular markers, they've created a screening tool that can distinguish baseline marker expression from true senescence induction in these challenging cancer subtypes. The challenge with basal-like cancers is that they constitutively express many canonical senescence markers even while proliferating, making it nearly impossible to determine if a therapeutic compound has actually induced growth arrest.
IMO here ML actually delivers the biggest benefit today: by developing a machine learning classifier based on morphological features rather than molecular markers, they've created a screening tool that can distinguish baseline marker expression from true senescence induction in these challenging cancer subtypes. The challenge with basal-like cancers is that they constitutively express many canonical senescence markers even while proliferating, making it nearly impossible to determine if a therapeutic compound has actually induced growth arrest.