New Machine Learning Method Precisely Maps 3D Genome Structure in Individual Cells
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Researchers at Carnegie Mellon University developed a machine learning method, scGHOST, to detect 3D genome subcompartments in individual cells.
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scGHOST enhances data quality to precisely pinpoint how chromosomes are spatially organized in the cell nucleus.
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Knowing 3D genome structure is important for understanding gene regulation and disease.
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scGHOST builds on the researchers' previous Higashi method for analyzing single-cell genome data.
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The tool could open new ways to study gene regulation related to health and disease at the individual cell level.