Research Papers:

ndmaSNF: cancer subtype discovery based on integrative framework assisted by network diffusion model

Chao Yang, Shu-Guang Ge and Chun-Hou Zheng _

PDF  |  HTML  |  Supplementary Files  |  How to cite

Oncotarget. 2017; 8:89021-89032. https://doi.org/10.18632/oncotarget.21643

Metrics: PDF 1675 views  |   HTML 2051 views  |   ?  


Chao Yang1, Shu-Guang Ge2 and Chun-Hou Zheng1

1College of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China

2College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China

Correspondence to:

Chun-Hou Zheng, email: [email protected]

Keywords: cancer subtyping, integrative method, network diffusion, somatic mutation data

Received: July 03, 2017     Accepted: August 27, 2017     Published: October 06, 2017


Recently, with the rapid progress of high-throughput sequencing technology, diverse genomic data are easy to be obtained. To effectively exploit the value of those data, integrative methods are urgently needed. In this paper, based on SNF (Similarity Network Diffusion) [1], we proposed a new integrative method named ndmaSNF (network diffusion model assisted SNF), which can be used for cancer subtype discovery with the advantage of making use of somatic mutation data and other discrete data. Firstly, we incorporate network diffusion model on mutation data to make it smoothed and adaptive. Then, the mutation data along with other data types are utilized in the SNF framework by constructing patient-by-patient similarity networks for each data type. Finally, a fused patient network containing all the information from different input data types is obtained by using a nonlinear iterative method. The fused network can be used for cancer subtype discovery through the clustering algorithm. Experimental results on four cancer datasets showed that our ndmaSNF method can find subtypes with significant differences in the survival profile and other clinical features.

Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 21643