Olivia L. Riedling

This study compares phenotypic, metabolomic, and genomic variation between two Aspergillus species to determine factors distinguishing opportunistic pathogens from non-pathogens.

The paper links fungal secondary metabolites to their biosynthetic gene clusters using genomic and chemical similarity, identifying 22 known pairs and hypothesizing 38 others in Aspergillus fischeri.

The paper uses machine learning to analyze Aspergillus species distribution and predicts climate change impacts on their ranges and diversity.

This study uses machine learning to predict fungal secondary metabolite bioactivity from biosynthetic gene clusters, finding higher accuracy when combining bacterial and fungal data.