Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class.
CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules / Cestarelli, Valerio; Fiscon, Giulia; Felici, Giovanni; Bertolazzi, Paola; Weitschek, Emanuel. - In: BIOINFORMATICS. - ISSN 1367-4803. - STAMPA. - 35:5(2015), pp. 697-704. [10.1093/bioinformatics/btv635]
CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules
FISCON, GIULIA;FELICI, GIOVANNI;
2015
Abstract
Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class.File | Dimensione | Formato | |
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