An efficient semi-unsupervised gene selection method via spectral biclustering.
Gene selection is an important issue in microarray data processing. In this paper, we propose an efficient method for selecting relevant genes. First, we use spectral biclustering to obtain the best two eigenvectors for class partition. Then gene combinations are selected based on the similarity between the genes and the best eigenvectors. We demonstrate our semi-unsupervised gene selection method using two microarray cancer data sets, i.e., the lymphoma and the liver cancer data sets, where our method is able to identify a single gene or a two-gene combinations which can lead to predictions with very high accuracy.
Liu
Bing
B
School of Electrical and Electronic Engineering, Nanyang Technology University, Singapore 639798.
Wan
Chunru
C
Wang
Lipo
L
D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins