2023
DOI: 10.1101/2023.09.12.557468
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Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Olivia Riedling,
Allison S. Walker,
Antonis Rokas

Abstract: Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bio… Show more

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Cited by 2 publications
(2 citation statements)
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“…SSN descriptor attained lower accuracy than PFAM domains despite relying on them. This finding is also supported in [21] where it is shown to be redundant. Similarly, the classification accuracy of CARD descriptor is also low.…”
Section: Resultssupporting
confidence: 78%
See 1 more Smart Citation
“…SSN descriptor attained lower accuracy than PFAM domains despite relying on them. This finding is also supported in [21] where it is shown to be redundant. Similarly, the classification accuracy of CARD descriptor is also low.…”
Section: Resultssupporting
confidence: 78%
“…Recently, NPF tool was extended to the detection of fungal secondary metabolites. This NPFF tool [21] is trained on NPF dataset along with 314 new fungal BGCs.…”
Section: Introductionmentioning
confidence: 99%