Enhancing fluorescence correlation spectroscopy with machine learning to infer anomalous molecular motion.

The random motion of molecules in living cells has consistently been reported to deviate from standard Brownian motion, a behavior coined as "anomalous diffusion." To study this phenomenon in living cells, fluorescence correlation spectroscopy (FCS) and single-particle tracking (SPT) are the two main methods of reference. In opposition to SPT, FCS, with its classical analysis methodology, cannot consider models of motion for which no analytical expression of the auto-correlation function is known.