Protocol for non-invasive tumor monitoring and diagnosis based on interpretable deep learning
Abstract
Tumor-specific DNA methylation profiling in plasma cell-free DNA (cfDNA) offers a promising approach for non-invasive tumor detection. Here, we present a protocol that uses Oncoder, an interpretable deep-learning-based framework, to monitor treatment response by tracking dynamic changes in tumor-specific DNA methylation signals in patient plasma cfDNA. We describe steps for data preparation, performing differential methylation analysis, training Oncoder, and interpreting the model’s outputs. This protocol is versatile and adaptable to various data types and application scenarios. For complete details on the use and execution of this protocol, please refer to Yang et al. 1
Fields of Study & Subjects
Cancer Genomics and Diagnostics
Epigenetics and DNA Methylation
Gene expression and cancer classification
Publication details
- Journal / source
- STAR Protocols
- Volume
- 7
- Issue
- 1
- Pages
- 104358–104358
- Publication date
- 6 February 2026
- Language
- EN