Machine learning architecture to predict drug response based on cancer cell FLIM images

With different states of two intrinsic fluorophores, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), we developed a single-layer autoencoder (AE) for feature extraction, which outputs condensed features representing the full metabolic FLIM information with lower dimensionality. We also described distributions of AE features and fluorescence lifetime redox ratio (FLIRR) from single cells by Gaussian mixture models (GMM), and predicted the values of FLIRR based on feature data from each time point for the HeLa cell lines and Caucasian-American (LNCaP) prostate cancer cell lines by the polynomial regression model and the random forest regression model.