The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
An Efficient and Robust Statistical Modeling Approach to Discover Differentially Expressed Genes Using Genomic Expression Profiles
Open accessAbstract
We have developed a statistical regression modeling approach to discover genes that are differentially expressed between two predefined sample groups in DNA microarray experiments. Our model is based on well-defined assumptions, uses rigorous and well-characterized statistical measures, and accounts for the heterogeneity and genomic complexity of the data. In contrast to cluster analysis, which attempts to define groups of genes and/or samples that share common overall expression profiles, our modeling approach uses known sample group membership to focus on expression profiles of individual genes in a sensitive and robust manner. Further, this approach can be used to test statistical hypotheses about gene expression. To demonstrate this methodology, we compared the expression profiles of 11 acute myeloid leukemia (AML) and 27 acute lymphoblastic leukemia (ALL) samples from a previous study (Golub et al. 1999) and found 141 genes differentially expressed between AML and ALL with a 1% significance at the genomic level. Using this modeling approach to compare different sample groups within the AML samples, we identified a group of genes whose expression profiles correlated with that of thrombopoietin and found that genes whose expression associated with AML treatment outcome lie in recurrent chromosomal locations. Our results are compared with those obtained using t-tests or Wilcoxon rank sum statistics.
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Cited by
MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA
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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
(2018) · Cancer Genomics & Proteomics · doi:10.21873/cgp.20063 · Cited by 1,528
A survey on filter techniques for feature selection in gene expression microarray analysis
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The temporal expression profile of Mycobacterium tuberculosis infection in mice
Talaat, A. M., Lyons, R., Howard, S. T., & Johnston, S. A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0306023101 · Cited by 363
Feature selection methods for big data bioinformatics: A survey from the search perspective
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Metadata
- Authors
- Thomas, J. G., Olson, J. M., Tapscott, S. J., & Zhao, L. P.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2001-07-01
- Volume
- 11
- Issue
- 7
- Pages
- 1227-1236
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- OpenAlex
- W2167283581
- Cited by
- 316
- References
- 65
- Type
- posted content
- Language
- en
- Open access
- hybrid
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- Thomas, J. G., Olson, J. M., Tapscott, S. J., & Zhao, L. P. (2001). An Efficient and Robust Statistical Modeling Approach to Discover Differentially Expressed Genes Using Genomic Expression Profiles. Genome Research. https://doi.org/10.1101/gr.165101
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