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2016•Unpublished venueOpen access

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

Nicola Lazzarini, Jaume Bacardit

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Abstract

We present RGIFE, an heuristic for biomarkers identification applicable to labelled –omics data (e.g. control vs. case) for which a classification problem can be formulated. RGIFE is guided by the information extracted from machine learning models with the aim to identify minimal and highly predictive biomarker sets.

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What this paper is about

We present RGIFE, an heuristic for biomarkers identification applicable to labelled –omics data (e.g. control vs. case) for which a classification problem can be formulated. RGIFE is guided by the information extracted from machine learning models with the aim to identify minimal and highly predictive biomarker sets.

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Available abstract

We present RGIFE, an heuristic for biomarkers identification applicable to labelled –omics data (e.g. control vs. case) for which a classification problem can be formulated. RGIFE is guided by the information extracted from machine learning models with the aim to identify minimal and highly predictive biomarker sets.

Key concepts: Open peer review, Plant biology, Identification (biology), Feature (linguistics), Heuristic, Computational biology, Medicine, Physiology

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