New filter-based feature selection criteria for identifying differentially expressed genes

Lit Hsin Loo, Samuel Roberts, Leonid Hrebien, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We propose two new filter-based feature selection criteria for identifying differentially expressed genes, namely the average difference score (ADS) and the mean difference score (MDS). These criteria replace the serial noise estimator used in existing criteria by a parallel noise estimator. The result is better detection of changes in the variance of expression levels, which t-statistic type criteria tend to under-emphasize. We compare the performance of the new criteria to that of several commonly used feature selection criteria, including the Welch t-statistic, the Fisher correlation score, the Wilcoxon rank sum, and the Independently Consistent Expression discriminator, on synthetic data and real biological data obtained from acute lymphoblastic leukemia and acute myeloid leukemia patients. We find that ADS and MDS outperform the other criteria by exhibiting higher sensitivity and comparable specificity. ADS is also able to flag several biologically important genes that are missed by the Welch t-statistic.

Original languageEnglish (US)
Title of host publicationProceedings - ICMLA 2005
Subtitle of host publicationFourth International Conference on Machine Learning and Applications
Pages135-144
Number of pages10
DOIs
StatePublished - Dec 1 2005
Externally publishedYes
EventICMLA 2005: 4th International Conference on Machine Learning and Applications - Los Angeles, CA, United States
Duration: Dec 15 2005Dec 17 2005

Publication series

NameProceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications
Volume2005

Other

OtherICMLA 2005: 4th International Conference on Machine Learning and Applications
CountryUnited States
CityLos Angeles, CA
Period12/15/0512/17/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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