Document Type
Conference Proceeding
Publication Date
12-23-2019
Publication Title
BMC Medical Genomics
Publisher
BMC
Volume
12
Issue
10
First page number:
1
Last page number:
13
Abstract
Background: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. Results: We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which 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. Conclusions: Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.
Keywords
Cox-PASNet, Deep neural network, Survival analysis, Glioblastoma multiforme, Ovarian cancer
Disciplines
Computer Engineering | Data Storage Systems | Engineering
File Format
File Size
2.519 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Hao, J.,
Kim, Y.,
Mallavarapu, T.,
Oh, J.,
Kang, M.
(2019).
Interpretable Deep Neural Network for Cancer Survival Analysis by Integrating Genomic and Clinical Data.
BMC Medical Genomics, 12(10),
1-13.
BMC.
http://dx.doi.org/10.1186/s12920-019-0624-2