ArticleCancer medicine2025
Exploring Mechanisms and Biomarkers of Breast Cancer Invasion and Migration: An Explainable Gene-Pathway-Compounds Neural Network.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundsExploring the molecular features that drive breast cancer invasion and migration remains an important biological and clinical challenge. In recent years, the use of interpretable machine learning models has enhanced our understanding of the underlying mechanisms of disease progression.
methodsIn this study, we present a novel gene-pathway-compound-related sparse deep neural network (GPC-Net) for investigating breast cancer invasion and migration. The GPC-Net is an interpretable neural network model that utilizes molecular data to predict cancer status. It visually represents genes, pathways, and associated compounds involved in these pathways.
resultsCompared with other modeling methods, GPC-Net demonstrates superior performance. Our research identifies key genes, such as ADCY8, associated with invasive breast cancer and verifies their expression in breast cancer cells. In addition, we conducted a preliminary exploration of several pathways.
conclusionGPC-Net is among the pioneering deep neural networks that incorporate pathways and compounds, aiming to balance interpretability and performance. It is expected to offer a more convenient approach for future biomedical research.
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