Frequently-asked questions

Contents

1 Introductory questions
2 Questions About NPO Design and Development
3 Questions about NPO status and availability
1. 3.1 How is the NPO made available?

Introductory questions

What is NanoParticle Ontology (NPO)?

NanoParticle Ontology (NPO) is an ontology that is being developed to represent the knowledge underlying the description, preparation and characterization of nanomaterials in the area of cancer nanotechnology. The NPO is designed and developed within the framework of the Basic Formal Ontology. The development of NPO started with defining the terms and relationships used for describing the chemical composition, physicochemical and functional/biological characterization of nanomaterials (e.g., nanoparticles, nanodevices, nanostructures, etc.), which are formulated and tested for applications in cancer diagnostics and therapeutics.

Why develop the NPO?

There are ontologies / controlled vocabularies (e.g., Gene Ontology (GO), Chemical Entities of Biological Interest (ChEBI), NCI Thesaurus (EVS), etc.), which represent some parts of the knowledge within the domain of cancer nanotechnology. Nevertheless, an ontology that provides a unifying knowledge framework for cancer nanotechnology research has to be developed for the following purposes:

To provide terms for annotating data generated from research in cancer nanotechnology.
To provide the knowledge framework required for developing data sharing models and standards in nanomedicine.
To enable semantic integration of the data by providing the terms and relationships for data annotation.
To enable unambiguous interpretation of data pertaining to the description and characterization of nanomaterials.
To enable knowledge-based searching of the data for accessing and retrieving relevant information that facilitates comparison of nanomaterials and characterization results, leading towards knowledge enhancement and discovery.

The NanoParticle Ontology is being developed with these (above-listed) purposes in mind.

What is an ontology?

An ontology is a formal, explicit representation of knowledge belonging to a subject area: the knowledge is encoded and represented as a hierarchy of concepts (terms / classes) that are described using attributes (e.g., metadata such as preferred name, definition, synonyms, etc.), related using associative relations, and formalized using logical axioms in a machine-interpretable language (e.g., Ontology Web Language or OWL).

What are ontologies useful for?

Ontologies have applications as common vocabularies, which researchers from different disciplines can share for annotating data in texts as well as in databases. There are several advantages to using ontologies:

the explicit definitions of the terms help avoid ambiguities in the usage of terminologies and interpretation of results;
the logical relationships among the terms help to semantically integrate different parts of the annotated data, and to perform knowledge-based searches for accessing and retrieving the relevant data.

What is cancer nanotechnology research?

An intrinsically interdisciplinary field of research devoted to the development and application of nanotechnology-based methods in the treatment, diagnosis, and detection of cancer.

Why informatics methods are important for the advancement of cancer nanotechnology research?

Experimental data from cancer nanotechnology research are diverse and large in volume. Informatics methods are needed to efficiently use these data, and facilitate the realization of nanotechnology applications in personalized treatment methods.

Most of these data characterize the physicochemical and functional properties related to the in vitro / in vivo behavior of nanoparticles that are formulated for applications in cancer diagnostics and therapeutics. Small changes in chemical composition can cause drastic changes in the properties of nanoparticles. Since there are many combinatorial ways by which the chemical composition can be modified, one can formulate diverse types of nanoparticles with varying properties and applications. Each new formulation will require experimental characterizations and this in turn adds more volume and diversity to the data. Additionally, the data and the underlying knowledge in cancer nanotechnology are complex due to the integration of information from multidisciplinary areas such as chemistry, material science, biology, and cancer medicine.

Most of the experimental results are found in disparate sources like journal articles. Manually, it is difficult to process large amounts of information from textual sources. This difficulty limits the effective use of information for advancing research. Therefore, in order to effectively process and utilize the information, there is a need for informatics methods (e.g., grid-based database technologies, text-mining techniques, information models, controlled vocabularies, ontologies) that facilitate 1) integration, sharing and searching of data from disparate sources, 2) semantic integration of data, and 3) unambiguous interpretation of data. An important application of informatics is in the area of data mining and knowledge discovery. Cancer nanotechnology data sets are rich in information and this can be mined for structure-activity relationships, and to seek correlations between different characteristic nanoparticle properties (e.g., correlation between in vitro and in vivo properties). Mining of existing literature data can provide useful information to guide the re-purposing or de novo design of nanoparticles. There are database resources such as caNanoLab, which are being developed for storing, searching and sharing data generated from characterization experiments, with the goal of enabling knowledge discovery. However, databases must be complemented by a common vocabulary to facilitate semantic interoperability among them.

Questions About NPO Design and Development

What information was used to initially construct the NPO?

To construct the NPO, we created an initial list of terms using the descriptions of nanoparticle formulations in the literature. These terms were obtained using information related to the:

type of chemical components of a nanoparticle formulation which include the nanoparticle, active chemical constituents of the nanoparticle, and functionalizing components;
molecular structure of the chemical components (e.g., atom, element, compound,liposome, micelle,etc.);
biochemical role or function of these chemical components (e.g., anticancer drug, surface modifying agent,MRI contrast agent,spacer, etc.);
type of nanoparticle based on its structure, function or chemical composition (e.g.,quantum dot, solid lipid nanoparticle, iron oxide nanoparticle, biodegradable nanoparticle, nanotube, gold nanocantilever, etc.);
chemical linkages between chemical components (e.g., amide linkage,disulphide linkage, encapsulation, etc.);
physical locations of chemical components within a nanoparticle (e.g., core,surface, etc.);
nanoparticle shape (e.g., spherical, cylindrical, etc.);
physical state of the formulation (e.g., emulsion, hydrogel, etc.);
physical, chemical, or functional properties of chemical constituents and functionalizing agents (e.g., organic, hydrophilic, magnetic, etc.);
applications in cancer diagnosis, therapy, and treatment (e.g., chemotherapy, diagnostic imaging, detection of cancer cells,etc.);
underlying mechanisms guiding the design for the formulation (e.g., endocytosis, active targeting, etc.);
type of stimulus (e.g., magnetic field, ultrasound, pH change, etc.) for activating the function of nanoparticles , and the response to that stimulus (e.g., drug release from nanoparticle in response to magnetic field, heat generation from nanoparticle in response to infrared light, etc.).

How was the initial list of terms generated for the NPO?

Specifically, for each type of information, we identified the header terms and relationships associating these terms. These terms and relationships provided a structure for organizing the information content in the literature, based on which we collected more terms and organized them in the form of a taxonomic “is_a” hierarchy.

For formal development of the NPO, we re-factored this hierarchy of terms using terms from the Basic Formal Ontology (BFO) at the upper-level of NPO, and constructed the NPO in the Ontology Web Language (OWL) using well-defined design principles. Terms that are found in other relevant ontologies / controlled vocabularies like GO, ChEBI, and NCI Thesaurus are re-used in NPO.

What design factors are considered in the development of NPO?

BFO as the upper-level ontology for NPO

The BFO (Basic Formal Ontology) was selected as the upper-level ontology for developing a structured classification of NPO terms. BFO is a formal ontology based on tested principles for biomedical ontologies. The reasons for using BFO as the upper-level ontology are as follows:

it provides a formal structure for the classification of domain terms;
it offers well-defined design principles that are known for best ontology practices in the biomedical area;
it facilitates interoperability with other ontologies having the formal structure of BFO; and,
it allows a clear, unambiguous and rigorous expansion of the ontology via collaborative development.

For a comprehensive account of the BFO, the reader is directed to the BFO manual. Only relevant BFO terms are currently used in the NPO; however, other top-level BFO terms may be added if needed.

OWL as the encoding language for NPO

We have selected OWL-DL as the language for encoding the NPO. This is because

OWL has formal semantics and additional vocabularies that facilitate machine interoperability;
it is designed for use in applications that process information as well as to present information to humans (http://www.w3.org/TR/owl-features/), and;
of the availability of Protégé-OWL editor, which has an intuitive design for editing OWL files and greatly facilitates collaborative ontology development by both ontologists and domain experts.

Main design principles

The main design principles used in developing the NPO are listed below. These design precepts are based on BFO and Open Biomedical Ontologies (OBO) Foundry principles (http://www.obofoundry.org/crit.shtml) as well as our review of other OWL-encoded ontologies and controlled vocabularies:

Principle of unbiased representation: Following BFO design principles, any term in the ontology should represent an entity as known in reality and not represent it from the biased view of an individual.
Principle of asserted single “is_a” inheritance: Again following BFO principles, each term should have no more than one parent term in the asserted OWL hierarchy. This principle offers the advantages of making the ontology easily extensible and interoperable with other ontologies that have a formal structure. This single-parent structure also helps to build the ontology in a modular fashion whereby different parts of the ontology can be worked on independently.
Principle of inferred multiple “is_a” inheritance: Multiple parent-child relationships for a term are not present in the asserted hierarchy. However, a term can have more than one parent in the inferred hierarchy that is constructed by invoking an appropriate “OWL reasoner” (e.g., Pellet) on the asserted hierarchy. Rules for inferring these relationships are expressed using OWL description logics and specified as OWL necessary and sufficient or necessary conditions, in the ontology. The OWL reasoner uses the OWL expressions to create the inferred hierarchy.
Sibling disjointedness: Unlike in the BFO, axioms for disjoint sibling classes are not enforced at all levels in the asserted OWL hierarchy. The disjointedness is maintained at the upper level of the ontology formed by the BFO classes. If sibling disjointedness is applied at a level of the asserted OWL hierarchy, then the following principles are considered:

Disjointedness is applied only between primitive sibling classes.
Disjoint axioms are applied to primitive sibling classes only after the hierarchical level containing the classes is exhausted, such that any class added later will not have instances that overlap with the instances of existing sibling classes.

Preferred name and definition: Every OWL class and OWL property (object, datatype) must have a preferred name and a textual definition using the NPO’s OWL annotation properties: “preferred name” and “definition”.
Synonym: If a class or OWL property has multiple names, these names must be provided as synonyms using the NPO’s “synonym” OWL annotation property.
External class reference: Classes borrowed from external sources should be given their external reference ID using the “dBXrefID” annotation property. A term may have multiple dBXrefIDs if it has mappings to more than one terminology.
Code: Every class must have an identification code that starts with the prefix “NPO_” (e.g., NPO_100).
rdf:ID and rdf:Label: Every class specifically defined in the NPO must have its NPO code as its rdf:ID. The rdf:ID of every class borrowed from an external ontology found in the OBO Foundry list, must be preserved in the NPO. Every class in the NPO must also have its preferred name as its rdf:Label.

What is the current scope of NPO?

The NPO is developed to represent knowledge underlying the chemical composition, preparation, physicochemical and functional/biological characterization of nanomaterials in the cancer nanotechnology domain.

Questions about NPO status and availability

How is the NPO made available?

Public releases of the NPO are made available through the BioPortal web site, maintained by the National Center for Biomedical Ontology.

To visualize the latest version of NPO, please click here
To visualize or download all released versions of the NPO, please click here.
For release notes, please click here

The NPO is now included in the NCI metathesaurus (NCIm), which can be accessed at http://ncimeta.nci.nih.gov/ .

The NCI metathesaurus contains about 3,600,000 terms from over 76 vocabularies, and these terms are mapped to about 1,400,000 biomedical concepts. Terms from multiple vocabularies that are mapped to a single biomedical concept allows the user to choose from the multiple vocabularies to annotate data. Simultaneously, this facilitates discovery of vocabularies unknown to the user. By the inclusion of NPO into the NCI metathesaurus, we expect that NPO accessibility and usage will be extended within the NCIm; NPO will add semantics into the NCIm; and that NCIm users will be able to take advantage of the knowledge provided by NPO.

Comments