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- W4385663340 abstract "An original approach that adopts machine learning inference to predict protein structural information using hydrogen–deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS." @default.
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- W4385663340 date "2023-08-07" @default.
- W4385663340 modified "2023-09-26" @default.
- W4385663340 title "Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models" @default.
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- W4385663340 doi "https://doi.org/10.1021/jasms.3c00145" @default.
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