Ranking through integration of protein-similarity for identification of cell-cyclic genes.
Gene array experiments are progressively conducted. However, the biological functional interpretation has not kept pace with this rapid escalation. Functional genomics using data mining methods potentially offers precise, objective, and more reliable gene identification. Our work creates a gene-ranking scheme by integrating gene expression profile phase information with protein similarity to identify cell-cyclic genes. We present a unique schema to enable integration by employing QR-factorisation 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.
Department of Computer Science, Louisiana Tech University, Ruston, LA 71272, USA. sdua@coes.latech.edu
Sumeet
S
Dua
Pradeep
P
Chowriappa
Alan E
AE
Alex
D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D018816:Genes, cdc; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
D011506:Proteins