Penn researchers crack AI method to solve hidden gene equations
University of Pennsylvania researchers have developed a new AI method called 'Mollifier Layers' to solve complex equations driving gene expression. The innovation involves using mollifier layers to clean up noise in data, enabling the reliable reverse-engineering of inverse partial differential equations.
Researchers at the University of Pennsylvania have solved a persistent obstacle in computational mathematics by developing a new AI method called 'Mollifier Layers'. This innovation enables the reliable reverse-engineering of inverse partial differential equations, which describe the development of systems both spatially and temporally. The team, led by Vivek Shenoy, Eduardo D. Glandt President's Distinguished Professor in Materials Science and Engineering, applied this method to chromatin domains measuring 100 nanometres in length that regulate gene activation and deactivation. By adding mollifier layers, the researchers cleaned up noise in the data, unlocking epigenetic reaction rates controlling chromatin organisation and gene activity. Understanding these rates could inform therapies that redirect cells toward desired states during ageing, cancer progression, or development. The implications extend across materials science, fluid dynamics, and other fields where scientists must infer hidden parameters from noisy observations.
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