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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
*Corresponding author for this work
  • University of Mississippi
Scholary Output:
Contribution to journal
Article
Peer-review

Abstract

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-review

Original 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

1744-5458

Publication IDs

  • Scopus: 33748803096
  • PubMed: 18048164

Publication metrics

Metrics

SciVal
citations
4
SciVal
FWCI
0.13
SciVal
Author count
3
SciVal
Paper percentile
47
Scopus
citations
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