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Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction
Boris Vishnepolsky, Maya Grigolava, Grigol Managadze, Andrei Gabrielian, Alex Rosenthal, Darrell E. Hurt, Michael Tartakovsky, Malak Pirtskhalava
doi: https://doi.org/10.1101/2022.01.28.478081
Boris Vishnepolsky
1Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
Maya Grigolava
1Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
Grigol Managadze
1Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
Andrei Gabrielian
2Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
Alex Rosenthal
2Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
Darrell E. Hurt
2Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
Michael Tartakovsky
2Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
Malak Pirtskhalava
1Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
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Posted January 28, 2022.
Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction
Boris Vishnepolsky, Maya Grigolava, Grigol Managadze, Andrei Gabrielian, Alex Rosenthal, Darrell E. Hurt, Michael Tartakovsky, Malak Pirtskhalava
bioRxiv 2022.01.28.478081; doi: https://doi.org/10.1101/2022.01.28.478081
Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction
Boris Vishnepolsky, Maya Grigolava, Grigol Managadze, Andrei Gabrielian, Alex Rosenthal, Darrell E. Hurt, Michael Tartakovsky, Malak Pirtskhalava
bioRxiv 2022.01.28.478081; doi: https://doi.org/10.1101/2022.01.28.478081
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