Novel gene sets improve set-level classification of prokaryotic gene expression data.
BACKGROUND
Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers.


METHODS
We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach.


RESULTS
The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers.


CONCLUSIONS
Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.
Faculty of Electrical Engineering, Czech Technical University, Technická 2, Prague, 166 27, Czech Republic. holecm@fel.cvut.cz.
Matěj
M
Holec
Faculty of Electrical Engineering, Czech Technical University, Technická 2, Prague, 166 27, Czech Republic. kuzelo1@gmail.com.|School of Computer Science and Informatics, Cardiff University, Queen's Buildings, 5 The Parade, Roath, Cardiff, CF24 3AA, UK. kuzelo1@gmail.com.
Ondřej
O
Kuželka
Faculty of Electrical Engineering, Czech Technical University, Technická 2, Prague, 166 27, Czech Republic. zelezny@fel.cvut.cz.
Filip
F
Železný
D005786:Gene Expression Regulation; D063990:Gene Ontology; D000069550:Machine Learning; D053858:Metabolic Networks and Pathways / Q000235:genetics; D009876:Operon / Q000235:genetics; D011387:Prokaryotic Cells / Q000378:metabolism*; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D014157:Transcription Factors