Updating user profile using ontology based semantic similarity
Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics.
Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies.Each type of data explains the biological system under investigation from a specific point of view.In order to get full understanding of the system, however, one needs to integrate multiple types of data—typically coming from different laboratories and obtained using different experimental techniques.Since each category of methods has its own traits, it is indispensable to know which method is suitable for the application of interest.Motivated by this consideration, we summarize characteristics of each category of methods in this paper, provide a brief review of available software implementation of these methods, and introduce typical biological and biomedical applications that rely on ontologies.The plant ontology (PO) has been utilized to describe plant structures and growth stages .
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 .
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 tandem mass spectrometry, a large number of proteins can be sequenced and characterized rapidly .
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 .
By organizing concepts (terms) in a domain in a hierarchical way and describing relationships between terms using a small number of relational descriptors, an ontology supplies a standardized vocabulary for representing entities in the domain .