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Cox-PASNet

architecture

 Cox-PASNet, integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified.

This study was published in BIBM 2018, And journal version was published in BMC Medical Genomics:
J. Hao, Y. Kim, T. Mallavarapu, J.H. Oh, and M. Kang, "Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data", BMC Medical Genomics 12, 189 (2019) 


Citation

@article{Hao2019,
     authors = {Hao, Jie and Kim, Youngsoon and Mallavarapu, Tejaswini and Oh, Jung Hun
                and Kang, Mingon},
     doi = {10.1186/s12920-019-0624-2},
     issn = {17558794},
     journal = {BMC Medical Genomics}, 	
     title = {{Interpretable deep neural network for cancer survival analysis 
              by integrating genomic and clinical data}},
     year = {2019}
}
		

Source code: