Researchers at the University of California San Diego School of Medicine created a machine-learning method to predict chemotherapy resistance in cancer cells.
Cancer researchers have struggled to anticipate when cancer cells may withstand treatment; thus, the method is a major advance.
Chemotherapy treatments hinder cancer cells from copying their DNA, while the algorithm helps fast-growing tumor cells do so.
The algorithm found out how mutations cooperate or oppose DNA-copying drugs. They improved patient outcomes by using their algorithm to identify which cervical cancer tumors would react best to therapy.
It also identified cancers that may not react well. The computer software justified its choices by naming cervical cancer resistance protein groupings.
The researchers said the computer software must be able to explain its predictions to operate successfully and create trustworthy smart systems.
For many cancer patients, chemotherapy is lifesaving yet exhausting. It can reduce tumors, but it can also cause hair loss, tiredness, nausea, appetite changes, and low blood cell counts.
Scientists and medics can spot signs of resistance, but pinpointing the exact time is like hitting a target with a blindfold.The researchers' machine learning method may predict when tumors will reject treatment.
This technology should help researchers develop novel medicines, they say. Finally, UC San Diego School of Medicine researchers' machine learning system represents a cancer research triumph.
Chemotherapy resistance prediction might change cancer treatment. The system can detect malignancies that respond favorably to treatment, benefiting patients.
The researchers believe that this technique will result in the development of new cancer treatments.