Updating user profile using ontology based semantic similarity

08-Oct-2020 01:17

With tandem mass spectrometry, a large number of proteins can be sequenced and characterized rapidly [4].

Indeed, high-throughput experimental techniques have enabled the collection of a vast volume of omics data, while how to organize, interpret, and use these data has now become a serious issue [5].

The plant ontology (PO) has been utilized to describe plant structures and growth stages [10].

Particularly, in order to achieve the goal of providing standard annotations of multiple heterogeneous data sources using common controlled vocabularies, The open biological and biomedical ontologies (OBO) Foundry has been proposed to coordinate the development of ontologies in different biological and biomedical domains [5].

Further, we extend our review to software tools implementing these methods and applications using these methods.

Recent technical innovation in high-throughput experiments has been successfully bringing about a revolution in modern biological and biomedical studies.

With microarrays, expression levels of thousands of genes can be simultaneously measured [1].

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Participants of the OBO Foundry have agreed in advance on the adoption of a set of principles that specify the best practices for the development of ontologies, for the purpose of developing a set of interoperable humanly validated reference ontologies for all major domains of biomedical research.Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics.Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms.Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem.

With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms.

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