Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer.

ABSTRACT: The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer

SUBMITTER: Li Q 

PROVIDER: S-EPMC7883816 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature