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Recommending Bacterial Cultivation Conditions from 16S rRNA

Wang, Juntang et al.
arXiv (All) · Papers
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machine-learning
Recommending bacterial cultivation conditions from sequence data is a challenging structured problem characterized by missing standardized benchmarks and limited use of sequence structure. To address this, we introduce DSNI, a benchmark of 65,023 bacterial strains with phylogeny-aware splits, pairin
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