Mining housekeeping genes with a Naive Bayes classifier

doi:10.1186/1471-2164-7-277

BMC Genomics

Volume 7

Viewing options:

Abstract
Full text
PDF (415KB)
Additional files

Associated material:

Related literature:

Articles citing this article
on BioMed Central
on Google Scholar
on ISI Web of Science
on PubMed Central
Other articles by authors
on Google Scholar

De Ferrari L
Aitken S

on PubMed

De Ferrari L
Aitken S
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Post to:

Research article

Mining housekeeping genes with a Naive Bayes classifier

Luna De Ferrari and Stuart Aitken

School of Informatics, the University of Edinburgh, Edinburgh EH8 9LE, UK

author email corresponding author email

BMC Genomics 2006, 7:277doi:10.1186/1471-2164-7-277


Published:	30 October 2006

Abstract

Background

Traditionally, housekeeping and tissue specific genes have been classified using direct assay of mRNA presence across different tissues, but these experiments are costly and the results not easy to compare and reproduce.

Results

In this work, a Naive Bayes classifier based only on physical and functional characteristics of genes already available in databases, like exon length and measures of chromatin compactness, has achieved a 97% success rate in classification of human housekeeping genes (93% for mouse and 90% for fruit fly).

Conclusion

The newly obtained lists of housekeeping and tissue specific genes adhere to the expected functions and tissue expression patterns for the two classes. Overall, the classifier shows promise, and in the future additional attributes might be included to improve its discriminating power.