Mining housekeeping genes with a Naive Bayes classifier

Luna De Ferrari; Stuart Aitken

doi:10.1186/1471-2164-7-277

Mining housekeeping genes with a Naive Bayes classifier

Luna De Ferrari, Stuart Aitken

School of Chemistry

Research output: Contribution to journal › Article › peer-review

57 Citations (Scopus)

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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.

Original language	English
Article number	277
Number of pages	14
Journal	BMC Genomics
Volume	7
DOIs	https://doi.org/10.1186/1471-2164-7-277
Publication status	Published - 30 Oct 2006

Keywords

Normal human tissues
Expression
Database
Identification
Compendium

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10.1186/1471-2164-7-277

ferrari2006bmcgenomics277
© 2006 De Ferrari and Aitken; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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KW - Database

KW - Identification

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Mining housekeeping genes with a Naive Bayes classifier

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Keywords

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