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<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201612001

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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.

Key words： Radiosensitivity Omics Data Biological Network Radiosensitive Gene Signature

Received: 04 April 2016

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China (No.61402010,61374181), Natural Science Foundation of Anhui Province (No.1408085MF133)

About author:: (SHI Ming, born in 1988, Ph.D. candidate. His research interests include bioinformatics and pattern recognition.)(WANG Hongqiang, born in 1977, Ph.D., professor. His research interests include bioinformatics and pattern recognition.)(SUN Tingting, born in 1990, master student. Her research interests include bioinformatics and pattern recognition.)(XIE Xinping(Corresponding author), born in 1979, master, lecturer. Her research interests include applied mathematics and bioinformatics.)

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	SHI Ming
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Cite this article:

SHI Ming,WANG Hongqiang,SUN Tingting等. Identification of Radiosensitivity Gene Signatures Based on Transcriptomic Data and Biological Network[J]. , 2016, 29(12): 1057-1064.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201612001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I12/1057

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