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. C. 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

• Artificial intelligence and machine learning in cell-free-DNA-based diagnostics GENOME RESEARCH Tsui, W., Ding, S. C., Jiang, P., Lo, Y. 2025; 35 (1): 1-19

  Abstract

  The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.

  View details for DOI 10.1101/gr.278413.123

  View details for Web of Science ID 001669986300001

  View details for PubMedID 39843210

  View details for PubMedCentralID PMC11789496