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Creators/Authors contains: "Ito, Michael"

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  1. ; ; (, International Conference on Artificial Intelligence and Statistics, PMLR 258:2764-2772 (AISTATS))
    Free, publicly-accessible full text available May 5, 2026
  2. ; ; ; ; (, International Conference on Artificial Intelligence and Statistics, PMLR 258)
    Free, publicly-accessible full text available May 5, 2026
  3. ; ; (, IEEE)
    Advances in metagenomic sequencing have provided an unprecedented view of the microbial world, but untangling the web of microbe interdependencies and the complex relationship between microbiome and host is a major challenge in biology. New statistical methods are needed to analyze metagenomic data and infer these relationships. Focusing on amplicon sequencing data, we present methods for leveraging phylogenetic information in deep neural network models and for transfer learning from large data repositories. This approach is demonstrated in experiments using data from the Earth Microbiome Project (EMP) and a dataset of 1500 samples from Waimea Valley on the island of Oahu, Hawaii. 
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