TL;DR
DMR informed CpGPT finetuning achieved high internal discrimination and a higher mean test AUROC than the evaluated classical machine learning models.
Cancer of unknown primary (CUP) accounts for approximately 2–3 percent of all cancer diagnoses, with incidence declining due to improved diagnostic technologies. Accurate identification of tissue origin is critical for treatment selection and prognosis, yet conventional immunohistochemistry achieves only 65.6 percent a...
DNAmBERT, a Transformer-based deep learning framework designed to jointly model DNA sequence context and read-level methylation haplotype structure from cfDNA methylation sequencing data, indicates that integrated sequence–haplotype representation learning provides an accurate and scalable approach for cfDNA-based mult...
Fragmentia-AI™ WGS, a mutation-calling-independent framework that uses a transformer-based multiple-instance learning architecture with sequential fine-tuning across tumor fraction (TF) strata to extract latent cancer-associated signals from ULP-WGS data, enables robust cancer detection and clinically meaningful risk s...
DNA methylation profiling of cell-free DNA is increasingly used for tissue-of-origin analysis and cancer detection, but quantitative interpretation of enrichment-based sequencing data remains a major challenge. MeDIP-seq provides scalable, cost-effective profiling of low-input samples like cell-free DNA, but lacks...
It is demonstrated that methylation-based models can capture both diagnostic identity and clinically relevant disease stratification, supporting DNA methylation as a promising biomarker for further validation and potential clinical translation.
DNA methylation deregulation is an essential feature of tumor biology, influencing cancer formation and pathogenesis. Analyses of DNA methylation in bulk cancer specimens are challenging due to the high data dimensionality, noise, and mixture of different cell types. Here, we characterized methylation dynamics in cance...
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