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Analysis of dynamic light scattering data with sparse Bayesian learning for the study of cataractogenesis

Dynamic light scattering (DLS) experimental data is statistical in nature and therefore requires a probabilistic analysis tool. The probabilistic sparse Bayesian learning (SBL) algorithm is introduced for analyzing DLS data from ocular lenses. The algorithm is used to reconstruct the most-relevant size distribution of the α-crystallins and their aggregates. The performance of the algorithm is evaluated by analyzing simulated data from a known distribution and experimental DLS data from the ocular lenses of several mammals.