Results

JGI@25: Studying Sorghum’s Survival Skills
… Learn more on the EPICON project …
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Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning
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Information theory and machine learning illuminate large‐scale metabolomic responses of Brachypodium distachyon to environmental change
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Comparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics
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Partnerships: Adopt-A-Genome
… a paper. That was something that we have just never learned before ". — Angela (left) and Mark (right) …
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Genomic properties of Marine Group A bacteria indicate a role in the marine sulfur cycle
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Natural Prodcast Episode 18: A CSP Primer
… areas that are less of a focus, I would say. For example, marine research is not so much of a focus within BER … we have projects that are algal related or that are marine related, and so we can do some projects that are … appropriate than others. We heard, for example, that the marine env is specifically not of general interest to DOE or …
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Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits
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A sweet spot in marine ecosystems
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Lost in translation: What we have learned from attributes that do not translate from Arabidopsis to other plants
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Alpha‐ and beta‐mannan utilization by marine Bacteroidetes
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Protist impacts on marine cyanovirocell metabolism
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