Conference proceeding
New filter-based feature selection criteria for identifying differentially expressed genes
ICMLA 2005: FOURTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, v 2005
01 Jan 2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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2 citations in Scopus
Details
- Title
- New filter-based feature selection criteria for identifying differentially expressed genes
- Creators
- L H Loo - Bauer Center for Genomics Res., Harvard Univ., Cambridge, MA, USAS RobertsL Hrebien - Drexel UniversityM Kam - Drexel University
- Contributors
- M A Wani (Editor)M Milanova (Editor)L Kurgan (Editor)M Reformat (Editor)K Hafeez (Editor)
- Publication Details
- ICMLA 2005: FOURTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, v 2005
- Conference
- ICMLA 2005: FOURTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, 4th
- Publisher
- IEEE
- Number of pages
- 10
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; General Internal Medicine
- Web of Science ID
- WOS:000235473200020
- Scopus ID
- 2-s2.0-33847287864
- Other Identifier
- 991019173522204721
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- Web of Science research areas
- Computer Science, Artificial Intelligence