Oxford University Press
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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.
Convolutional neural networks; Predictive modeling; Disease outcomes; PathCNN; Pathway image; CNN model; Glioblastoma; Multi-omics data
Bioimaging and Biomedical Optics | Biomedical Engineering and Bioengineering | Engineering
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Deasy, J. O.
PathCNN: Interpretable Convolutional Neural Networks for Survival Prediction and Pathway Analysis Applied to Glioblastoma.
Oxford University Press.