A co-expression modules based gene selection for cancer recognition.
Gene expression profiles are used to recognize patient samples for cancer diagnosis and therapy. Gene selection is crucial to high recognition performance. In usual gene selection methods the genes are considered as independent individuals and the correlation among genes is not used efficiently. In this description, a co-expression modules based gene selection method for cancer recognition is proposed. First, in the cancer dataset a weighted correlation network is constructed according to the correlation between each pair of genes, different modules from this network are identified and the significant modules are selected for following exploration. Second, based on these informative modules information gain is applied to selecting the feature genes for cancer recognition. Then using LOOCV, the experiments with different classification algorithms are conducted and the results show that the proposed method makes better classification accuracy than traditional gene selection methods. At last, via gene ontology enrichment analysis the biological significance of the co-expressed genes in specific modules was verified.
School of Information Science and Engineering, Hunan University, Changsha 410082, China; College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. Electronic address: hnluxinguo@hnu.edu.cn.
Xinguo
X
Lu
School of Information Science and Engineering, Hunan University, Changsha 410082, China.
Yong
Y
Deng
School of Information Science and Engineering, Hunan University, Changsha 410082, China.
Lei
L
Huang
School of Information Science and Engineering, Hunan University, Changsha 410082, China.
Bingtao
B
Feng
School of Information Science and Engineering, Hunan University, Changsha 410082, China.
Bo
B
Liao
D000465:Algorithms; D001943:Breast Neoplasms / Q000378:metabolism; D045744:Cell Line, Tumor; D003110:Colonic Neoplasms / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D044127:Epigenesis, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D016403:Lymphoma, Large B-Cell, Diffuse / Q000378:metabolism; D008962:Models, Theoretical; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis; D040081:Protein Array Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine; D049490:Systems Biology
Cancer recognition; Gene expression data; WGCNA