Deep-learning models for lipid nanoparticle-based drug delivery.

Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. 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. The first modeling approach used a convolutional neural network extracting per-cell features at early time points. These features were then combined and explored using either a long short-term memory network (approach 2) or time series feature extraction and gradient boosting machines (approach 3). Accounting for the temporal dynamics significantly improved performance. The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high-content imaging.