Janna Hastings

Structure-based classification and ontology in chemistry

BackgroundRecent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is pentacyclic compound (compounds containing five-ring structures), while an example of a role-based class is analgesic, since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.ResultsWe analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.ConclusionSystems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.

Exploiting disjointness axioms to improve semantic similarity measures

MOTIVATIONnRepresenting domain knowledge in biology has traditionally been accomplished by creating simple hierarchies of classes with textual annotations. Recently, expressive ontology languages, such as Web Ontology Language, have become more widely adopted, supporting axioms that express logical relationships other than class-subclass, e.g. disjointness. This is improving the coverage and validity of the knowledge contained in biological ontologies. However, current semantic tools still need to adapt to this more expressive information. In this article, we propose a method to integrate disjointness axioms, which are being incorporated in real-world ontologies, such as the Gene Ontology and the chemical entities of biological interest ontology, into semantic similarity, the measure that estimates the closeness in meaning between classes.nnnRESULTSnWe present a modification of the measure of shared information content, which extends the base measure to allow the incorporation of disjointness information. To evaluate our approach, we applied it to several randomly selected datasets extracted from the chemical entities of biological interest ontology. In 93.8% of these datasets, our measure performed better than the base measure of shared information content. This supports the idea that semantic similarity is more accurate if it extends beyond the hierarchy of classes of the ontology.nnnCONTACTnjoao.ferreira@lasige.di.fc.ul.pt.nnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data.

OntoQuery: Easy-to-use web-based OWL querying

Summary: The Web Ontology Language (OWL) provides a sophisticated language for building complex domain ontologies and is widely used in bio-ontologies such as the Gene Ontology. The Protégé-OWL ontology editing tool provides a query facility that allows composition and execution of queries with the human-readable Manchester OWL syntax, with syntax checking and entity label lookup. No equivalent query facility such as the Protégé Description Logics (DL) query yet exists in web form. However, many users interact with bio-ontologies such as chemical entities of biological interest and the Gene Ontology using their online Web sites, within which DL-based querying functionality is not available. To address this gap, we introduce the OntoQuery web-based query utility. Availability and implementation:u2003The source code for this implementation together with instructions for installation is available at http://github.com/IlincaTudose/OntoQuery. OntoQuery software is fully compatible with all OWL-based ontologies and is available for download (CC-0 license). The ChEBI installation, ChEBI OntoQuery, is available at http://www.ebi.ac.uk/chebi/tools/ontoquery. Contact: hastings@ebi.ac.uk

Evaluating the Emotion Ontology through use in the self-reporting of emotional responses at an academic conference

BackgroundWe evaluate the application of the Emotion Ontology (EM) to the task of self-reporting of emotional experience in the context of audience response to academic presentations at the International Conference on Biomedical Ontology (ICBO). Ontology evaluation is regarded as a difficult task. Types of ontology evaluation range from gauging adherence to some philosophical principles, following some engineering method, to assessing fitness for purpose. The Emotion Ontology (EM) represents emotions and all related affective phenomena, and should enable self-reporting or articulation of emotional states and responses; how do we know if this is the case? Here we use the EM ‘in the wild’ in order to evaluate the EM’s ability to capture people’s self-reported emotional responses to a situation through use of the vocabulary provided by the EM.ResultsTo achieve this evaluation we developed a tool, EmOntoTag, in which audience members were able to capture their self-reported emotional responses to scientific presentations using the vocabulary offered by the EM. We furthermore asked participants using the tool to rate the appropriateness of an EM vocabulary term for capturing their self-assessed emotional response. Participants were also able to suggest improvements to the EM using a free-text feedback facility. Here, we present the data captured and analyse the EM’s fitness for purpose in reporting emotional responses to conference talks.ConclusionsBased on our analysis of this data set, our primary finding is that the audience are able to articulate their emotional response to a talk via the EM, and reporting via the EM ontology is able to draw distinctions between the audience’s response to a speaker and between the speakers (or talks) themselves. Thus we can conclude that the vocabulary provided at the leaves of the EM are fit for purpose in this setting. We additionally obtained interesting observations from the experiment as a whole, such as that the majority of emotions captured had positive valence, and the free-form feedback supplied new terms for the EM.AvailabilityEmOntoTag can be seen at http://www.bioontology.ch/emontotag; source code can be downloaded from http://emotion-ontology.googlecode.com/svn/trunk/apps/emontotag/and the ontology is available at http://purl.obolibrary.org/obo/MFOEM.owl.

Ontologies for human behavior analysis and their application to clinical data.

Mental and behavioral disorders are common in all countries and represent a significant portion of the public health burden in developed nations. The human cost of these disorders is immense, yet treatment options for sufferers are currently limited, with many patients failing to respond sufficiently to currently available interventions. Standardized terminologies facilitate data annotation and exchange for patient care, epidemiological analyses, and primary research into novel therapeutics. Such medical terminologies include SNOMED CT and ICD, which we describe here. Medical informatics is increasingly moving toward the adoption of formal ontologies, as they describe the nature of entities in reality and the relationships between them in such a fashion that they can be used for sophisticated automated reasoning and inference applications. An added benefit is that ontologies can be applied across different contexts in which traditionally separate domain-specific vocabularies have been used. In this chapter, we report on a suite of ontologies currently in development for the description of human behavior, mental functioning, and mental disorders, and discuss their application in clinical contexts. We focus on the benefits of ontologies for clinical data management and for facilitating translational research for the development of novel therapeutics to treat challenging and debilitating conditions.

Process attributes in bio-ontologies

BackgroundBiomedical processes can provide essential information about the (mal-) functioning of an organism and are thus frequently represented in biomedical terminologies and ontologies, including the GO Biological Process branch. These processes often need to be described and categorised in terms of their attributes, such as rates or regularities. The adequate representation of such process attributes has been a contentious issue in bio-ontologies recently; and domain ontologies have correspondingly developed ad hoc workarounds that compromise interoperability and logical consistency.ResultsWe present a design pattern for the representation of process attributes that is compatible with upper ontology frameworks such as BFO and BioTop. Our solution rests on two key tenets: firstly, that many of the sorts of process attributes which are biomedically interesting can be characterised by the ways that repeated parts of such processes constitute, in combination, an overall process; secondly, that entities for which a full logical definition can be assigned do not need to be treated as primitive within a formal ontology framework. We apply this approach to the challenge of modelling and automatically classifying examples of normal and abnormal rates and patterns of heart beating processes, and discuss the expressivity required in the underlying ontology representation language. We provide full definitions for process attributes at increasing levels of domain complexity.ConclusionsWe show that a logical definition of process attributes is feasible, though limited by the expressivity of DL languages so that the creation of primitives is still necessary. This finding may endorse current formal upper-ontology frameworks as a way of ensuring consistency, interoperability and clarity.

Structured chemical class definitions and automated matching for chemical ontology evolution

Ontologies encode the knowledge of human experts in order to allow computers to automate common tasks in a domain. They are hierarchically organised and backed by computational logic which allows automated inferences of the implicit consequences of explicitly stated knowledge. ChEBI is a database and ontology of chemical entities of biological interest [1]. Within the ontology, chemical entities are classified based on shared structural features and also based on their roles and activities in biological systems. For example, the chemical class ‘aminopyridine’ is defined as ‘Compounds containing a pyridine skeleton substituted by one or more amine groups’, while an example of a role based class is ‘antiviral drug’, which groups together chemical entities that are used as antiviral drugs, regardless of their chemical structure. We have developed a novel semi-automated system for creating structure-based chemical class definitions. Our tool allows curators to draw and visually define shared structural features for classes of chemicals, which definitions are then used to automatically detect class membership across the full chemical database. The front end is based on an extended JChemPaint [2] and the Google Web Toolkit, and the back-end on a custom extension of the Chemistry Development Kit [3]. With this tool, it is possible to define chemical classes based on molecular skeletons, substitute groups, arbitrary parts including cycles of arbitrary length, formulae and overall properties, and these features can be combined using nested logical operators. Matching these definitions to candidate structures from the database is accomplished by means of an in-memory matching procedure, validated against the existing manually curated classification in ChEBI, allowing us to iteratively refine both the definitions of classes as well as to evolve the quality of the classification in ChEBI.