Identification of Radiosensitivity Gene Signatures Based on Transcriptomic Data and Biological Network
SHI Ming1,2, WANG Hongqiang2, SUN Tingting2, XIE Xinping3
1.Machine Intelligence & Computational Biology Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031 2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079 3.School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230022
Abstract：Accurate radiosensitivity prediction of tumor patients is crucial in tumor treatment. In this paper, a method is proposed for predicting tumor radiosensitivity based on high-throughput omics data in combination with biological networks. Firstly, Spearman correlation scores are calculated between gene expression profiles and tumor cell survival fraction at 2 Gy (SF2). Then, these scores are refined based on random walk theory by the topology of prior biological networks, such as gene networks or protein-to-protein interaction networks. Finally, highly significant radiosensitivity genes are screened out as feature variables to learn support vector machine(SVM) classifiers for predicting radiosensitivity of tumor patients. Experimental results on real-world microarrays datasets demonstrate the effectiveness of the proposed algorithm.
时明，王红强，孙婷婷，谢新平. 融合生物网络拓扑特性的肿瘤放疗敏感基因信号识别方法*[J]. 模式识别与人工智能, 2016, 29(12): 1057-1064.
SHI Ming, WANG Hongqiang, SUN Tingting, XIE Xinping. Identification of Radiosensitivity Gene Signatures Based on Transcriptomic Data and Biological Network. , 2016, 29(12): 1057-1064.
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2016, Vol. 29 
