Sakuntha D. Gunarathna
@sakuntha.bsky.social
Biomedical Sciences Researcher (PhD Candidate) at the School of Medicine, University of North Dakota, USA
LinkedIn : linkedin.com/in/sakuntha-gunarathna
ResearchGate: researchgate.net/profile/Sakuntha_Gunarathna
LinkedIn : linkedin.com/in/sakuntha-gunarathna
ResearchGate: researchgate.net/profile/Sakuntha_Gunarathna
Thrilled our 2nd cfDNA study is officially published in Communications Medicine! 🤩
We show cfDNA profiling captures treatment-associated chromatin changes, distinguishes treatment sensitivity & reveals mechanisms linked to CDK4/6 inhibition and Polycomb/EZH2 pathways.
doi.org/10.1038/s438...
We show cfDNA profiling captures treatment-associated chromatin changes, distinguishes treatment sensitivity & reveals mechanisms linked to CDK4/6 inhibition and Polycomb/EZH2 pathways.
doi.org/10.1038/s438...
May 8, 2026 at 10:54 PM
Thrilled our 2nd cfDNA study is officially published in Communications Medicine! 🤩
We show cfDNA profiling captures treatment-associated chromatin changes, distinguishes treatment sensitivity & reveals mechanisms linked to CDK4/6 inhibition and Polycomb/EZH2 pathways.
doi.org/10.1038/s438...
We show cfDNA profiling captures treatment-associated chromatin changes, distinguishes treatment sensitivity & reveals mechanisms linked to CDK4/6 inhibition and Polycomb/EZH2 pathways.
doi.org/10.1038/s438...
Officially published in Communications Biology!
In this study, we developed an open chromatin-guided, interpretable machine learning pipeline to uncover cancer-specific nucleosomal fragment patterns directly from cell-free DNA.
📄 Published article: doi.org/10.1038/s420...
In this study, we developed an open chromatin-guided, interpretable machine learning pipeline to uncover cancer-specific nucleosomal fragment patterns directly from cell-free DNA.
📄 Published article: doi.org/10.1038/s420...
November 13, 2025 at 5:54 AM
Officially published in Communications Biology!
In this study, we developed an open chromatin-guided, interpretable machine learning pipeline to uncover cancer-specific nucleosomal fragment patterns directly from cell-free DNA.
📄 Published article: doi.org/10.1038/s420...
In this study, we developed an open chromatin-guided, interpretable machine learning pipeline to uncover cancer-specific nucleosomal fragment patterns directly from cell-free DNA.
📄 Published article: doi.org/10.1038/s420...
Excited to share our preprint "ATAC-seq Guided Interpretable Machine Learning Reveals Cancer-Specific Chromatin Features in Cell-free DNA"
Excited to share our first preprint on cell-free DNA! Led by our talented grad student Sakuntha, we have developed a machine learning pipeline guided by ATAC-seq to identify unique nucleosomal fragment patterns in cancer patients. www.researchsquare.com/article/rs-5...
ATAC-seq Guided Interpretable Machine Learning Reveals Cancer-Specific Chromatin Features in Cell-free DNA
Cell-free DNAs (cfDNAs) are DNA fragments found in blood. In healthy individuals, cfDNAs are primarily derived from immune cells, while in cancer patients, a significant fraction of cfDNAs originates ...
www.researchsquare.com
January 16, 2025 at 2:24 AM
Excited to share our preprint "ATAC-seq Guided Interpretable Machine Learning Reveals Cancer-Specific Chromatin Features in Cell-free DNA"