https://www.selleckchem.com/pr....oducts/fen1-in-4.htm
and spread of resistance. https//doi.org/10.1289/EHP7484.Background Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. Aim Using time-lapse data of HepG2 cells exposed to lipid nanoparticles loaded with mRNA for expression of GFP, the authors hypothesized that it is possible to predict in advance whether a cell will express GFP. Methods The first modeling approach used a convolutional neural network extracting per-cell features at early time points. These features were then c
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