PathCNN: Interpretable Convolutional Neural Networks for Survival Prediction and Pathway Analysis Applied to Glioblastoma

Authors

Jung Hun Oh, Memorial Sloan-Kettering Cancer Center
Wookjin Choi, Virginia State University
Euiseong Ko, University of Nevada, Las Vegas
Mingon Kang, University of Nevada, Las VegasFollow
Allen Tannenbaum, Stony Brook University
Joseph O. Deasy, Memorial Sloan-Kettering Cancer Center

Document Type

Article

Publication Date

7-12-2021

Publication Title

Bioinformatics

Publisher

Oxford University Press

Volume

37

First page number:

i443

Last page number:

i450

Abstract

Motivation: Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly. Results: To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease.

Keywords

Convolutional neural networks; Predictive modeling; Disease outcomes; PathCNN; Pathway image; CNN model; Glioblastoma; Multi-omics data

Disciplines

Bioimaging and Biomedical Optics | Biomedical Engineering and Bioengineering | Engineering

File Format

pdf

File Size

602 KB

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Repository Citation

Oh, J., Choi, W., Ko, E., Kang, M., Tannenbaum, A., Deasy, J. O. (2021). PathCNN: Interpretable Convolutional Neural Networks for Survival Prediction and Pathway Analysis Applied to Glioblastoma. Bioinformatics, 37 i443-i450. Oxford University Press.
http://dx.doi.org/10.1093/bioinformatics/btab285