By Peter N. Robinson
Introduction to Bio-Ontologies explores the computational historical past of ontologies. Emphasizing computational and algorithmic concerns surrounding bio-ontologies, this self-contained textual content is helping readers comprehend ontological algorithms and their applications.
The first a part of the publication defines ontology and bio-ontologies. It additionally explains the significance of mathematical common sense for knowing strategies of inference in bio-ontologies, discusses the likelihood and facts subject matters valuable for realizing ontology algorithms, and describes ontology languages, together with OBO (the preeminent language for bio-ontologies), RDF, RDFS, and OWL.
The moment half covers major bio-ontologies and their purposes. The e-book provides the Gene Ontology; upper-level ontologies, akin to the elemental Formal Ontology and the Relation Ontology; and present bio-ontologies, together with numerous anatomy ontologies, Chemical Entities of organic curiosity, series Ontology, Mammalian Phenotype Ontology, and Human Phenotype Ontology.
The 3rd a part of the textual content introduces the key graph-based algorithms for bio-ontologies. The authors talk about how those algorithms are utilized in overrepresentation research, model-based tactics, semantic similarity research, and Bayesian networks for molecular biology and biomedical applications.
With a spotlight on computational reasoning subject matters, the ultimate half describes the ontology languages of the Semantic net and their purposes for inference. It covers the formal semantics of RDF and RDFS, OWL inference ideas, a key inference set of rules, the SPARQL question language, and the cutting-edge for querying OWL ontologies.
Software and information designed to counterpoint fabric within the textual content can be found at the book’s site: http://bio-ontologies-book.org the location presents the R Robo package deal built for the publication, besides a compressed archive of information and ontology records utilized in many of the routines. It additionally deals teaching/presentation slides and hyperlinks to different suitable websites.
This booklet offers readers with the basis to exploit ontologies as a kick off point for brand spanking new bioinformatics learn initiatives or to aid present molecular genetics study initiatives. via providing a self-contained advent to OBO ontologies and the Semantic internet, it bridges the distance among either fields and is helping readers see what every one can give a contribution to the research and figuring out of biomedical data.
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Extra resources for Introduction to Bio-Ontologies (Chapman & Hall CRC Mathematical & Computational Biology)
In that case, we write S ⊧ t. Inference, on the other hand, is the process by which we determine the sentences that are entailed by the existing sentences in the knowledge base. 2 Propositional Logic A mathematical logic consists of a syntax, or a set of rules for forming sentences, as well as the semantics of the logic, which are another set of rules for assigning truth values to the sentences of the logics. In this chapter, we will discuss three kinds of mathematical logic that are relevant for understanding inference in ontologies.
Entails a sentence t if t is true whenever every sentence in S is true. In that case, we write S ⊧ t. Inference, on the other hand, is the process by which we determine the sentences that are entailed by the existing sentences in the knowledge base. 2 Propositional Logic A mathematical logic consists of a syntax, or a set of rules for forming sentences, as well as the semantics of the logic, which are another set of rules for assigning truth values to the sentences of the logics. In this chapter, we will discuss three kinds of mathematical logic that are relevant for understanding inference in ontologies.
On the other hand, (∃x)P means that P (xi ) must be true for some i, and thus (∃x)P is equivalent to P (x1 ) ∨ P (x2 ) ∨ . . ∨ P (xn ). 4) ∧ and ∨ are dual to one another; thus, it is possible to express a universal quantifier using only the existential quantifier and vice versa: ¬((∀x)P ) = (∃x)¬P ¬((∃x)P ) = (∀x)¬P However, usually both types of quantifier are used for clarity’s sake. Often, the ∃ quantifier is used in patterns such as (∃x)(P (x) ∧ Q(x)), which expresses that there is at least one individual x that has both properties P and Q.
Introduction to Bio-Ontologies (Chapman & Hall CRC Mathematical & Computational Biology) by Peter N. Robinson