Gene Ontology Driven Feature Filtering from Microarray Data

Gene Ontology Driven Feature Filtering from Microarray Data

article in a collection out of WoS and Scopus

Czech

Microarray data is high-dimensional and noisy. Dimension reduction, i.e. selecting a small number of genes, is an effective way to improve mining efficiency. We propose a novel approach that integrates gene ontology knowledge at the level of feature selection into microarray data to improve binary class prediction. The advantage of this filtering approach lies in considering gene-to-gene relations and selecting more meaningful features comparing to the methods evaluating genes in isolation. In addition, gene ontology knowledge can overcome the limitations of noisy microarray data. Our approach is evaluated on a real benchmark dataset.

Microarray data is high-dimensional and noisy. Dimension reduction, i.e. selecting a small number of genes, is an effective way to improve mining efficiency. We propose a novel approach that integrates gene ontology knowledge at the level of feature selection into microarray data to improve binary class prediction. The advantage of this filtering approach lies in considering gene-to-gene relations and selecting more meaningful features comparing to the methods evaluating genes in isolation. In addition, gene ontology knowledge can overcome the limitations of noisy microarray data. Our approach is evaluated on a real benchmark dataset.

feature selection, gene ontology, microarray data, class prediction

ŠILHAVÁ, J.; SMRŽ, P.

2010

1. 1. 2010

Jindřichův Hradec

978-80-245-1636-3

Znalosti 2010

9th Annual Conference, Jindřichův Hradec

263

266

4