PathCNN: Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma Conference Paper
| Authors: | Oh, J. H.; Choi, W.; Ko, E.; Kang, M.; Tannenbaum, A.; Deasy, J. O. |
| Title: | PathCNN: Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma |
| Conference Title: | ISMB/ECCB 2021: 29th Conference on Intelligent Systems for Molecular Biology and the 20th European Conference on Computational Biology |
| 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. © 2021 The Author(s). Published by Oxford University Press. |
| Keywords: | glioblastoma; image processing, computer-assisted; image processing; software; humans; human; neural networks, computer |
| Journal Title | Bioinformatics |
| Volume: | 37 |
| Issue: | Suppl. 1 |
| Conference Dates: | 2021 Jul 25-30 |
| Conference Location: | Virtual |
| ISBN: | 1367-4803 |
| Publisher: | Oxford University Press |
| Date Published: | 2021-07-01 |
| Start Page: | i443 |
| End Page: | i450 |
| Language: | English |
| DOI: | 10.1093/bioinformatics/btab285 |
| PUBMED: | 34252964 |
| PROVIDER: | scopus |
| PMCID: | PMC8336441 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 September 2021 -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work