A Hybrid Tumor Gene Selection Method with Laplacian Score and Correlation Analysis

In the proposed method, Laplacian criteria is firstly introduced to sort the genes as their descending scores. And then, correlation analysis is applied to select those pathogenic genes from the sorted sequence to reduce the redundancy. At last, SVM classifier is used to predict the class labels of the optimal gene subset. Compared to some other related gene selection methods such as Fisher score and Laplacian score, Experimental results on four standard datasets have shown the stability and efficiency of the proposed method.

[1]  C. Viroli,et al.  Supervised locally linear embedding for classification : an application to gene expression data analysis Supervised locally linear embedding in problemi di classificazione : un ’ applicazione all ’ analisi di dati di espressione genica , 2005 .

[2]  Juan Miguel García-Gómez,et al.  Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors , 2013, Comput. Biol. Medicine.

[3]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[4]  Francesca Martella,et al.  Classification of microarray data with factor mixture models , 2006, Bioinform..

[5]  Bo Li,et al.  Locally linear representation Fisher criterion based tumor gene expressive data classification , 2014, Comput. Biol. Medicine.

[6]  Johan A. K. Suykens,et al.  Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction , 2004, Bioinform..

[7]  J.C. Rajapakse,et al.  SVM-RFE With MRMR Filter for Gene Selection , 2010, IEEE Transactions on NanoBioscience.

[8]  Gersende Fort,et al.  Classification using partial least squares with penalized logistic regression , 2005, Bioinform..

[9]  Lei Wang,et al.  On Similarity Preserving Feature Selection , 2013, IEEE Transactions on Knowledge and Data Engineering.

[10]  Jian Yang,et al.  Sparse maximum margin discriminant analysis for feature extraction and gene selection on gene expression data , 2013, Comput. Biol. Medicine.

[11]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  I. Jolliffe.  Principal Component Analysis , 2002 .

[13]  De-Shuang Huang,et al.  Independent component analysis-based penalized discriminant method for tumor classification using gene expression data , 2006, Bioinform..

[14]  Feiping Nie,et al.  Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.

[15]  Shili Lin,et al.  Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach , 2004, Bioinform..