TY - JOUR
T1 - Domain intelligible models
AU - Imangaliyev, Sultan
AU - Prodan, Andrei
AU - Nieuwdorp, Max
AU - Groen, Albert K.
AU - van Riel, Natal A. W.
AU - Levin, Evgeni
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85052729287&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/29981382
U2 - https://doi.org/10.1016/j.ymeth.2018.06.011
DO - https://doi.org/10.1016/j.ymeth.2018.06.011
M3 - Article
C2 - 29981382
SN - 1046-2023
VL - 149
SP - 69
EP - 73
JO - Methods (San Diego, Calif.)
JF - Methods (San Diego, Calif.)
ER -