Author

Christina M. Payne

University of KentuckyORCID 0000-0001-5264-0964Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Assay:protein sequence· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Gregg T. BeckhamiD· 2 shared Japheth E. GadoiD· 2 shared Brent E. HarrisoniD· 1 shared Jerry StåhlbergiD· 1 shared Mats O.J. SandgreniD· 1 shared

Activity by Year

2020-2021 · peak 1/yr

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5 top collaborators · 10 mutual co-author links

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2 shared papersBioinformatics

Gregg T. BeckhamORCID

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2 papers

Recent·Most cited

BioinformaticsJournal of Biological ChemistryPeer-reviewedJul 1, 2021

Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases

Japheth E. Gado, Brent E. Harrison, Mats O.J. Sandgren…·Christina M. Payne

This study uses machine learning to identify sequence features that predict functional subtypes and CBM presence in GH7 glycoside hydrolases with high accuracy.

32 citations

protein sequencemachine learning (classical)

𝕏 PostBluesky

BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedJul 8, 2020

Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning

Japheth E. Gado, Gregg T. Beckham, Christina M. Payne

This study improves enzyme optimum temperature prediction by applying ensemble learning and resampling strategies to address data imbalance, reducing error on high Topt values and increasing overall R2.

64 citations

machine learning (classical)benchmarking

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