Identification of hair cycle-associated genes from time-course gene expression profile using fractal analysis
- Sunil K. Mathur(corresponding author),
- Atul M. Doke,
- Ajit Sadana
- University of Mississippi
Scholary Output:
Contribution to journal
Article
Peer-reviewAbstract
Microarray technology permits one to monitor thousands of processes going on inside the cell. This tool has been used to study gene expression profiles associated with the hair-growth cycle. We provide a novel method called the fractal analysis method to identify hair-growth cycle associated genes from time course gene expression profiles. Fractal analysis is a much better method than the computational method used by Lin et al. (2004). The fractal dimension obtained by fractal analysis process also indicates the irregularity in hair-growth pattern. The computational method used by Lin et al. (2004) was unable to make any inference about the hair-growth pattern.
Publication Information
Output type
Scholary Output:
Contribution to journal
Article
Peer-reviewOriginal language
English (US)Pages from-to (Number of pages)
Pages 249-258 (10 pages)Journal (Volume, Issue Number)
International Journal of Bioinformatics Research and Applications (Volume 2, Issue 3)Publication milestones
- Published - 2006
Publication status
Published - 2006
ISSN
1744-5458Publication IDs
- Scopus: 33748803096
- PubMed: 18048164
Publication metrics
Metrics
SciVal
citations
4
SciVal
FWCI
0.13
SciVal
Author count
3
SciVal
Paper percentile
47
Fractional count
1
Fractional count
0.33
Fractional count
2
Fractional count
0.67
Fractional count
1
Fractional count
1
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Captures
4
Citation count
4
