Domain intelligible models

Sultan Imangaliyev, Andrei Prodan, Max Nieuwdorp, Albert K Groen, Natal A.W. van Riel, Evgeni Levin

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
199 Downloads (Pure)


Mining biological information from rich "-omics" datasets is facilitated by organizing features into groups that are related to a biological phenomenon or clinical outcome. For example, microorganisms can be grouped based on a phylogenetic tree that depicts their similarities regarding genetic or physical characteristics. Here, we describe algorithms that incorporate auxiliary information in terms of groups of predictors and the relationships between them into the metagenome learning task to build intelligible models. In particular, our cost function guides the feature selection process using auxiliary information by requiring related groups of predictors to provide similar contributions to the final response. We apply the developed algorithms to a recently published dataset analyzing the effects of fecal microbiota transplantation (FMT) in order to identify factors that are associated with improved peripheral insulin sensitivity, leading to accurate predictions of the response to the FMT.

Original languageEnglish
Pages (from-to)69-73
Number of pages5
Early online date4 Jul 2018
Publication statusPublished - 1 Oct 2018


  • artificial intelligence
  • machine learning
  • Metagenomics
  • Algorithms
  • Models, Biological
  • Humans
  • Gastrointestinal Microbiome/physiology
  • Phylogeny
  • Metagenome/physiology


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