RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers.

ABSTRACT:

Background

Current -omics technologies are able to sense the state of a biological sample in a very wide variety of ways. Given the high dimensionality that typically characterises these data, relevant knowledge is often hidden and hard to identify. Machine learning methods, and particularly feature selection algorithms, have proven very effective over the years at identifying small but relevant subsets of variables from a variety of application domains, including -omics data. Many methods exist with varying trade-off between the size of the identified variable subsets and the predictive power of such subsets. In this paper we focus on an heuristic for the identification of biomarkers called RGIFE: Rank Guided Iterative Feature Elimination. RGIFE is guided in its biomarker identifi

SUBMITTER: Lazzarini N 

PROVIDER: S-EPMC5493069 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature