What is it about?
Expression of genes in tumors can be used to predict whether patients will respond to platin chemotherapies before they are treated. This study describes how we use supervised machine learning to make these predictions
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Photo by National Cancer Institute on Unsplash
Why is it important?
The decision as to which chemotherapy should be used is currently not personalized to the response that is expected from that therapy. Current personalization strategies focus on mutations that may be present or other descriptions of the tumor, but not expected response.
Perspectives
One of the interesting findings in our article is that the composition of genes that predict outcome can vary depending on the level of chemotherapy resistance.
Dr Peter K Rogan
Western University
Read the Original
This page is a summary of: Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning, Signal Transduction and Targeted Therapy, January 2019, Springer Science + Business Media,
DOI: 10.1038/s41392-018-0034-5.
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