getDNB: identifying dynamic network biomarkers of hepatocellular carcinoma from time-varying gene regulations utilizing graph embedding techniques for anomaly detection.

Abstract

Motivation: Early detection and timely intervention of hepatocellular carcinoma (HCC) are pivotal for improving patient prognosis. Current diagnostic approaches often detect HCC at later stages, thereby diminishing treatment efficacy. Recent advancements in high-throughput sequencing technology have vastly improved the identification of molecular markers via biological networks. However, existing methodologies frequently overlook the intricate gene interaction information in temporal gene regulatory networks. Therefore, our study proposes an algorithm model, getDNB, leveraging graph embedding technique (get) for anomaly detection in time-varying dynamic networks. The model aims to facilitate early HCC detection and propel precision medicine by recognizing dynamic network biomarker (DNB).

Results: We proposed the getDNB model, which utilizes graph convolutional networks for graph embedding, mapping high-dimensional gene regulatory networks to low-dimensional feature vector spaces. By calculating gene anomaly degrees through an outlier score, and using the minimum dominant set algorithm alongside with the shortest path algorithm, we discovered DNBs and their associated networks in HCC. The getDNB model successfully pinpointed 33 HCC DNBs, effectively differentiating various temporal stages of HCC progression, and demonstrated robustness across numerous real HCC datasets. Functional enrichment analysis unveiled that these DNBs play critical roles in HCC occurrence and development, outperforming widely used feature selection algorithms.

Availability and implementation: The source code and data can be found at https://github.com/zpliulab/getDNB.