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DTSTAMP:20210916T132454Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210708T180000
DTEND;TZID=Europe/Stockholm:20210708T183000
UID:submissions.pasc-conference.org_PASC21_sess127_msa271@linklings.com
SUMMARY:Automated Chemical Ontology Expansion for Semantic Integration in 
 Metabolism
DESCRIPTION:Minisymposium\n\nAutomated Chemical Ontology Expansion for Sem
 antic Integration in Metabolism\n\nHastings\n\nSmall molecular metabolites
  are increasingly being recognised as fundamental regulators of biological
  processes. Technologies such as metabolomics are able to quantify hundred
 s of metabolites in biological samples. However, methods for interpretatio
 n of metabolic differences lag behind other -omics layers, as metabolite a
 nnotation resources are fragmented and incomplete. ChEBI is a chemical ont
 ology semantically classifying metabolites based on structural and functio
 nal features, which is used for annotations in multiple biological databas
 es, and is interlinked with the Gene Ontology. However, ChEBI is incomplet
 e, and as a manually maintained resource, growth is slow. In this contribu
 tion, I will present an automated ontology extension approach that uses an
  ensemble of machine learning approaches to automatically classify small m
 olecules into the ChEBI ontology based on their associated chemical struct
 ures.\n\nDomain: CS and Math, Chemistry and Materials, Life Sciences
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