A Self-attention Graph Convolutional Network for Precision Multi-tumor Early Diagnostics with DNA Methylation Data

AbstractDNA methylation-based precision tumor early diagnostics is emerging as state-of-the-art technology that could capture early cancer signs 3  ~ 5 years in advance, even for clinically homogenous groups. Presently, the sensitivity of early detection for many tumors is ~ 30%, which needs significant improvement. Nevertheless, based on the genome-wide DNA methylation data, one could comprehensively characterize tumors’ entire mol ecular genetic landscape and their subtle differences. Therefore, novel high-performance methods must be modeled by considering unbiased information using excessively available DNA methylation data. To fill this gap, we have designed a computational model involving a self-attention graph convolution al network and multi-class classification support vector machine to identify the 11 most common cancers using DNA methylation data. The self-attention graph convolutional network automatically learns key methylation sites in a data-driven way. Then, multi-tumor early diagnostics is realized by train ing a multi-class classification support vector machine based on the selected methylation sites. We evaluated our model’s performance through several data sets of experiments, and our results demonstrate the effectiveness of the selected key methylation sites, which are highly relevant for blood d iagnosis.Graphical AbstractThe pipeline of the self-attention graph convolutional network based computational framework.
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research