Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning is a research paper published in Microbiology Spectrum (2024). On theSindex it has a DataRank of 0.762. It has been cited 37 times, with 30 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
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Base Score Contribution
0.546
From this paper's citation signal
Citation Network Contribution
0.217
From 18 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 30 citers.
HHS | NIH | National Institute of Allergy and Infectious Diseases
Grant: R01 AI153356
National Science Foundation
Grant: DEB-2110404
HHS | NIH | National Institute of General Medical Sciences
Grant: R35 GM146987
Burroughs Wellcome Fund
Grant: N/A
National Institutes of Health
Grant: 5R01AI153356-04
Deciphering the phenotypic and genomic traits that underlie the evolution of pathogenicity differences among Aspergillus fumigatus and its close relatives
National Institutes of Health
Grant: 3R35GM146987-02S2
Machine learning approaches for the discovery, repurposing, and optimization of natural products with therapeutic potential - Supplement to support grad training of Adrian Russ
National Science Foundation
Grant: 2110404
Collaborative Research: RoL: The Evolution of the Genotype-Phenotype Map across Budding Yeasts
FWCI
11.98
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Supplementary figures, tables, and code used to train models and produce input files for "Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning" (Riedling et al. 2023).
Code used to train models and produce input files for "Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning" (Riedling et al. 2023).
Supplementary figures, tables, and code used to train models and produce input files for "Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning" (Riedling et al. 2023).
Supplemental tables and figures for "Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning" (Riedling et al. 2023).
Supplemental tables and figures for "Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning" (Riedling et al. 2023).