Gated graph neural network-based features for epigenetic target detection

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Abstract

Epigenetics refers to genetic modifications that cause changes in gene expression and are not encoded in DNA. These modifications, including histone, DNA, and RNA, as well as miRNA, are closely related to oncology research and have the potential to influence the treatment of metabolic, neurological, inflammatory, and cardiovascular diseases. In cancer treatment, targeting abnormal epigenetic regulation can be achieved through inhibition of abnormal DNA methylation and histone methylation and acetylation. However, the discovery of efficient epigenetic drugs often requires substantial time and resources. To aid in drug research, we propose an extraction method of molecule features based on a gated graph neural network (GGNN) and integrate four supervised classifiers to predict epigenetic targets in screened chemogenomic datasets. Our GGNN+XGBoost integrated model achieves an overall accuracy of 0.8158, with accuracy increasing to 0.981 in certain target datasets. This approach offers a promising solution for predicting epigenetic targets, with implications for cancer treatment and other diseases. Furthermore, our method showcases the potential of utilizing big datasets and predictive models to expedite epigenetic drug discovery.

Keywords

GGNN / XGBoost / epigenetic targets / supervised classifiers / drug discovery

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