Author

Darrell E. Hurt

National Institutes of HealthORCID 0000-0002-9829-8567Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

Alex RosenthaliD· 1 shared Andrei E. GabrielianiD· 1 shared Boris VishnepolskyiD· 1 shared Grigol Managadze· 1 shared Malak Pirtskhalava· 1 shared Maya Grigolava· 1 shared Michael TartakovskyiD· 1 shared

Activity by Year

2022-2022 · peak 1/yr

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7 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Alex RosenthalORCID

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1 paper

Recent·Most cited

Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction

Boris Vishnepolsky, Maya Grigolava, Grigol Managadze, Andrei E. Gabrielian, Alex Rosenthal, Darrell E. Hurt, Michael Tartakovsky, Malak Pirtskhalava

The paper compares machine learning algorithms for predicting antimicrobial peptides (AMPs) specific to microbial strains, finding Random Forest and AdaBoost perform best with improved accuracy using genome characteristics.

Briefings in Bioinformatics · Jun 20, 2022Peer-reviewedBioinformatics