Spencer C. Ding

All Publications

• Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing COMMUNICATIONS BIOLOGY Hu, X., Shi, Y., Cheng, S., Huang, Z., Zhou, Z., Shi, X., Zhang, Y., Liu, J., Ma, M. L., Ding, S. C., Deng, J., Qiao, R., Peng, W., Choy, L., Yu, S. Y., Lam, W., Chan, K., Li, H., Jiang, P., Lo, Y. 2025; 8 (1): 606

  Abstract

  We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we constructed a hybrid model with CNN and transformer layers, named HK model 2. We improve the area under the receiver operating characteristic curve (AUC) for 5mC detection from 0.91 for HK model 1 to 0.99 for HK model 2. We further demonstrate that HK model 2 can detect other types of base modifications, such as 5-hydroxymethylcytosine (5hmC) and N6-methyladenine (6mA). Using HK model 2 to analyze 5mC patterns of cell-free DNA (cfDNA) molecules, we demonstrate the enhanced detection of patients with hepatocellular carcinoma, with an AUC of 0.97. Moreover, HK model 2-based detection of 6mA enables the detection of jagged ends of cfDNA and the delineation of cellular chromatin structures. HK model 2 is thus a versatile tool expanding the applications of single molecule real-time sequencing in liquid biopsies.

  View details for DOI 10.1038/s42003-025-08009-8

  View details for Web of Science ID 001466473100001

  View details for PubMedID 40229481

  View details for PubMedCentralID PMC11997116