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Neural nets supplant marker genes in analyzing single cell RNA sequencing


November 13, 2018

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Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing. This finding, published in the online journal Nature Communications, could help researchers identify new cell subtypes and differentiate between healthy and diseased cells. Full Story

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Carnegie Mellon University

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