Identification of hair cycle-associated genes from time-course gene expression profile using fractal analysis

Sunil K. Mathur, Atul M. Doke, Ajit Sadana

Research output: Contribution to journalArticle

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)249-258
Number of pages10
JournalInternational Journal of Bioinformatics Research and Applications
Issue number3
StatePublished - Jan 1 2006



  • Anagen
  • Catagen
  • Gene expression
  • Hair-cycle

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

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