KAUST DepartmentBio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
KAUST Grant NumberFCC/1/1976-04
Permanent link to this recordhttp://hdl.handle.net/10754/663430
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AbstractOntologies have long been employed in the life sciences to formally represent and reason over domain knowledge, and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in biomedical ontologies, and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.Key pointsOntologies provide background knowledge that can be exploited in machine learning models.Ontology embeddings are structure-preserving maps from ontologies into vector spaces and provide an important method for utilizing ontologies in machine learning. Embeddings can preserve different structures in ontologies, including their graph structures, syntactic regularities, or their model-theoretic semantics.Axioms in ontologies, in particular those involving negation, can be used as constraints in optimization and machine learning to reduce the search space.
CitationKulmanov, M., Smaili, F. Z., Gao, X., & Hoehndorf, R. (2020). Machine learning with biomedical ontologies. doi:10.1101/2020.05.07.082164
SponsorsThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, URF/1/3790-01-01, FCC/1/1976-04, FCC/1/1976-06, FCC/1/1976-17, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, FCC/1/1976-26, and URF/1/3450-01.
PublisherCold Spring Harbor Laboratory
Except where otherwise noted, this item's license is described as Archived with thanks to Cold Spring Harbor Laboratory