Biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data.

ABSTRACT: High-throughput technologies such as transcriptomics, proteomics, and metabolomics show great promise for the discovery of biomarkers for diagnosis and prognosis. Selection of the most promising candidates between the initial untargeted step and the subsequent validation phases is critical within the pipeline leading to clinical tests. Several statistical and data mining methods have been described for feature selection: in particular, wrapper approaches iteratively assess the performance of the classifier on distinct subsets of variables. Current wrappers, however, do not estimate the significance of the selected features. We therefore developed a new methodology to find the smallest feature subset which significantly contributes to the model performance, by using a combination of resampl

SUBMITTER: Rinaudo P 

PROVIDER: S-EPMC4914951 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature