ArticleSTAR protocols2026
Protocol for non-invasive tumor monitoring and diagnosis based on interpretable deep learning.
Article in STAR protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
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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.
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Registered trials
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