Abstract
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.
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Acknowledgement
This work was partly supported by the grants of Natural Science Foundation of China (61273303, 61273225, 61373109 and 61572381), China Postdoctoral Science Foundation (20100470613 and 201104173), Natural Science Foundation of Hubei Province (2010CDB03302), the Research Foundation of Education Bureau of Hubei Province (Q20121115).
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Li, B., Lei, XH., Hu, Y., Zhang, XL. (2016). A Hybrid Tumor Gene Selection Method with Laplacian Score and Correlation Analysis. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_21
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DOI: https://doi.org/10.1007/978-3-319-42291-6_21
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