Constructing the gene regulation-level representation of microarray data for cancer classification.
In this paper, we propose a regulation-level representation for microarray data and optimize it using genetic algorithms (GAs) for cancer classification. Compared with the traditional expression-level features, this representation can greatly reduce the dimensionality of microarray data and accommodate noise and variability such that many statistical machine-learning methods now become applicable and efficient for cancer classification. Experimental results on real-world microarray datasets show that the regulation-level representation can consistently converge at a solution with three regulation levels. This verifies the existence of the three regulation levels (up-regulation, down-regulation and non-significant regulation) associated with a particular biological phenotype. The ternary regulation-level representation not only improves the cancer classification capability but also facilitates the visualization of microarray data.
Wong
Hau-San
HS
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China.
Wang
Hong-Qiang
HQ
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins