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JGI Applies Machine Learning to Fungal Genomes

July 21, 2020

Support Vector Machine (SVM)-based prediction of lifestyle based on 6 gene clusters showed a >95 % accuracy in correctly predicting plant pathogens (blue) vs saprobes (green). (From Haridas S et al. Studies in Mycology, 2020)
Support Vector Machine (SVM)-based prediction of lifestyle based on 6 gene clusters showed a >95 % accuracy in correctly predicting plant pathogens (blue) vs saprobes (green). (From Haridas S et al. Studies in Mycology, 2020)

A team led by JGI researchers has generated a more accurate phylogenetic tree tracking the evolution of Dothideomycetes fungi. This work was enabled in part through JGI’s Community Science Program (CSP), as several approved proposals have contributed toward filling gaps on the fungal Tree of Life, including the 1000 Fungal Genomes Project. Researchers had access to over 100 Dothideomycetes genomes, just enough of a sample size to test if a computer algorithm could distinguish between fungal lifestyles of saprobes and pathogens based on the data provided. The work appeared in the June issue of Studies in Mycology. The work appeared in the June issue of Studies in Mycology.  Read more on the JGI website.

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