ArticleFrontiers in medicine2021
A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 14 citations in OpenAlex.
- AI/ML-Driven Gene Analysis: New Perspectives on Variant Calling in Normal Human Tissue Using scRNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A novel sequence-based transformer model architecture for integrating multi-omics data in preterm birth risk prediction.NPJ digital medicine · 2025Article
- Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics.Journal of experimental & clinical cancer research : CR · 2024Article
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.
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