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Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction
2011
Advances in Bioinformatics
Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant
doi:10.1155/2011/958129
pmid:22007208
pmcid:PMC3189455
fatcat:dredqf3dq5dghmzfcjrtu2wzea