research-article

resnetCox: A Residual Neural Network Method for High-throughput Survival Analysis

Authors:

Xianglin HuangAuthors Info & Claims

ICBBT '21: Proceedings of the 2021 13th International Conference on Bioinformatics and Biomedical Technology

Pages 123 - 128

https://doi.org/10.1145/3473258.3473277

Published: 11 December 2021 Publication History

Abstract

The Cox proportional hazards model is usually used in medical research for analyzing the association between the survival time of patients and one or more predictor variables. However, nonlinear, high-dimensional, low-sample size survival data cause computational challenges in survival analysis. An artificial neural network (ANN) is used for modeling nonlinear problems. A residual neural network is a specific type of neural network. In this paper, we build Cox proportional hazards model based on a residual neural network architecture, which we call it resnetCox model. The residual mapping of the resnetCox is two layers of fully connected network. The identity mapping of the resnetCox is a linear projection of inputs. The model is trained with the negative log likelihood of Cox model by Adam optimizer. It is implemented on the PyTorch in Python. We demonstrate the performance of resnetCox on BRCA, LUAD, KIRC and LUSC datasets from The Cancer Genome Atlas. We compare it with the other 3 models and experiments show that resnetCox has better performance than others in prediction accuracy and biological relevance.

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Cited By

Yin QChen WWu RWei Z(2022)Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)10.1109/ICNSC55942.2022.10004157(1-6)Online publication date: 15-Dec-2022
https://doi.org/10.1109/ICNSC55942.2022.10004157

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resnetCox: A Residual Neural Network Method for High-throughput Survival Analysis

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ICBBT '21: Proceedings of the 2021 13th International Conference on Bioinformatics and Biomedical Technology

May 2021

293 pages

ISBN:9781450389655

DOI:10.1145/3473258

Copyright © 2021 ACM.

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Publication History

Published: 11 December 2021

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the Fundamental Research Funds for the Central University

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ICBBT '21

ICBBT '21: 2021 13th International Conference on Bioinformatics and Biomedical Technology

May 21 - 23, 2021

Xi'an, China

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Cited By

Yin QChen WWu RWei Z(2022)Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)10.1109/ICNSC55942.2022.10004157(1-6)Online publication date: 15-Dec-2022
https://doi.org/10.1109/ICNSC55942.2022.10004157

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