Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data

Qingyan Yin, Wangwang Chen, Ruiping Wu, Zhi Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis.

Original languageEnglish (US)
Title of host publicationICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control
Subtitle of host publicationAutonomous Intelligent Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665472432
DOIs
StatePublished - 2022
Externally publishedYes
Event19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022 - Shanghai, China
Duration: Dec 15 2022Dec 18 2022

Publication series

NameICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control: Autonomous Intelligent Systems

Conference

Conference19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022
Country/TerritoryChina
CityShanghai
Period12/15/2212/18/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Optimization

Keywords

  • Cox proportional hazards
  • prognostic genes
  • residual neural network
  • survival analysis

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