Identification of hair cycle-associated genes from time-course gene expression profile using fractal analysis.
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.
Department of Mathematics, University of Mississippi, MS 38677, USA. skmathur@olemiss.edu
Sunil K
SK
Mathur
Atul M
AM
Doke
Ajit
A
Sadana
D000818:Animals; D016000:Cluster Analysis; D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods*; D017709:Fractals; D015870:Gene Expression; D006197:Hair / Q000502:physiology*; D007700:Kinetics; D051379:Mice; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011485:Protein Binding; D012984:Software; D013997:Time Factors