Ranking through Integration of Protein-Similarity for Identification of
Cell-cyclic Genes
Sumeet Dua, Pradeep Chowriappa and
Alan Alex
Gene array experiments are being progressively
conducted. However, the biological functional interpretation
using (semi-)automated discovery routines have not kept pace
with this rapid escalation. Functional genomics using data mining
methods potentially offers precise, objective, and more reliable
gene identification. The goal of our work is to create a generanking
scheme by integrating the phase information of gene
expression profiles with protein similarity to identify cell- cyclic
genes. We hypothesize that the identification of cell-cyclic genes
could be enhanced by integrating gene phase and primary protein
sequence similarity to every other gene in the dataset. Comparing
two sequences according to the properties of their residues may
highlight regions of sequence similarity emphasizing only
identities in the alignment. Those regions (sub-sequence) that may
have diverged will not draw attention away from any remaining
common features. We present a unique schema to enable such
integration by employing QR-factorization from the pair-wise
similarity matrix formulation. Angular coefficients are derived
and consequently employed for integrated gene ranking.
Experimental results on an independent benchmark dataset
signify the efficacy of the method when compared to previous
results in the area.
Index Terms
Microarray, cell-cyclic genes, gene-phase, proteinphase, periodicity.