Downloads provided by UsageCountsSupplementary data for "Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily"
Robinson, Serina L.; Smith, Megan D.; Richman, Jack E.; Aukema, Kelly G.; Wackett, Larry P.;
Supplementary data for "Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily"
Funding: We thank the U.S. Department of Energy Joint Genome Institute for synthetic DNA. The work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported under Contract No. DE-AC02-05CH11231. S.L.R. is supported by the National Science Foundation Graduate Research Fellowship (00039202). M.D.S. acknowledges support from a National Institutes of Health Biotechnology training grant (5T32GM008347-27). We also acknowledge support from the MnDRIVE initiative for Industry and the Environment.
Supplementary data for "Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily"
- University of Minnesota Morris United States
machine learning, substrate specificity, enzyme activity screen, p-nitrophenyl esters, thiolase
machine learning, substrate specificity, enzyme activity screen, p-nitrophenyl esters, thiolase
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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