Based on a highly detailed materials characterisation database (including atomistic and multiscale modelling), single and univariate statistical methods, combined with machine learning techniques, revealed key descriptors of biological functions.
Abstract
Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.
Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure)
