South Korean Scientists Use AI to Uncover Sources of Cell Variability Relevant to Improving Cancer Treatment
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A team of South Korean scientists used machine learning to uncover sources of cell variability in the human body.
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They developed a methodology called Density Physics-informed Neural Networks (Density-PINNs) to analyze cell signaling systems.
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By applying Density-PINNs, they found parallel signaling structure reduces cell heterogeneity, which is relevant to cancer treatment.
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This mathematical modeling research could lead to better understanding of cellular heterogeneity crucial for improving cancer therapies.
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The research is published in Patterns journal with the title “Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction.”