Significant random signatures reveals new biomarker for breast cancer.

ABSTRACT: BACKGROUND:In 2012, Venet et al. proposed that at least in the case of breast cancer, most published signatures are not significantly more associated with outcome than randomly generated signatures. They suggested that nominal p-value is not a good estimator to show the significance of a signature. Therefore, one can reasonably postulate that some information might be present in such significant random signatures. METHODS:In this research, first we show that, using an empirical p-value, these published signatures are more significant than their nominal p-values. In other words, the proposed empirical p-value can be considered as a complimentary criterion for nominal p-value to distinguish random signatures from significant ones. Secondly, we develop a novel computational method to extract

SUBMITTER: Saberi Ansar E 

PROVIDER: S-EPMC6842262 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature