Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles
Inter-tumour heterogeneity is one of cancer's most fundamental features. Patient stratification
based on drug response prediction is hence needed for effective anti-cancer therapy.
However, lessons from the past indicate that single-gene markers of response are rare
and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning
(ML) is emerging as a particularly promising complementary approach to precision
oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) …
based on drug response prediction is hence needed for effective anti-cancer therapy.
However, lessons from the past indicate that single-gene markers of response are rare
and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning
(ML) is emerging as a particularly promising complementary approach to precision
oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) …
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