“…While general-purpose kernels can be applied to protein inputs, there are also kernels designed for use on proteins, including spectrum and mismatch string kernels, 30,31 which count the number of shared subsequences between two proteins, and weighted decomposition kernels, 32 which account for three-dimensional protein structure. Support vector machines have been used to predict protein thermostability, 33,34,35,36,25,27,26,37 enzyme enantioselectivity, 38 and membrane protein expression and localization. 39 Gaussian process models combine kernel methods with Bayesian learning to produce probabilistic predictions.…”