Fahim T. Imam

Development and use of Ontologies Inside the Neuroscience Information Framework: A Practical Approach

An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF’s production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies.

The Gene Ontology (GO) Cellular Component Ontology: integration with SAO (Subcellular Anatomy Ontology) and other recent developments

BackgroundThe Gene Ontology (GO) (http://www.geneontology.org/) contains a set of terms for describing the activity and actions of gene products across all kingdoms of life. Each of these activities is executed in a location within a cell or in the vicinity of a cell. In order to capture this context, the GO includes a sub-ontology called the Cellular Component (CC) ontology (GO-CCO). The primary use of this ontology is for GO annotation, but it has also been used for phenotype annotation, and for the annotation of images. Another ontology with similar scope to the GO-CCO is the Subcellular Anatomy Ontology (SAO), part of the Neuroscience Information Framework Standard (NIFSTD) suite of ontologies. The SAO also covers cell components, but in the domain of neuroscience.DescriptionRecently, the GO-CCO was enriched in content and links to the Biological Process and Molecular Function branches of GO as well as to other ontologies. This was achieved in several ways. We carried out an amalgamation of SAO terms with GO-CCO ones; as a result, nearly 100 new neuroscience-related terms were added to the GO. The GO-CCO also contains relationships to GO Biological Process and Molecular Function terms, as well as connecting to external ontologies such as the Cell Ontology (CL). Terms representing protein complexes in the Protein Ontology (PRO) reference GO-CCO terms for their species-generic counterparts. GO-CCO terms can also be used to search a variety of databases.ConclusionsIn this publication we provide an overview of the GO-CCO, its overall design, and some recent extensions that make use of additional spatial information. One of the most recent developments of the GO-CCO was the merging in of the SAO, resulting in a single unified ontology designed to serve the needs of GO annotators as well as the specific needs of the neuroscience community.

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.

A survey of the neuroscience resource landscape: perspectives from the neuroscience information framework.

The number of available neuroscience resources (databases, tools, materials, and networks) available via the Web continues to expand, particularly in light of newly implemented data sharing policies required by funding agencies and journals. However, the nature of dense, multifaceted neuroscience data and the design of classic search engine systems make efficient, reliable, and relevant discovery of such resources a significant challenge. This challenge is especially pertinent for online databases, whose dynamic content is largely opaque to contemporary search engines. The Neuroscience Information Framework was initiated to address this problem of finding and utilizing neuroscience-relevant resources. Since its first production release in 2008, NIF has been surveying the resource landscape for the neurosciences, identifying relevant resources and working to make them easily discoverable by the neuroscience community. In this chapter, we provide a survey of the resource landscape for neuroscience: what types of resources are available, how many there are, what they contain, and most importantly, ways in which these resources can be utilized by the research community to advance neuroscience research.

Computational Neuroscience Ontology: a new tool to provide semantic meaning to your models

The diversity of modeling approaches in computational neuroscience makes model sharing, retrieval, reuse and reproducibility difficult and even sometimes impossible. To address this problem, standardized languages have been developed by and for the community, such as NeuroML[1], PyNN [2] and NineML (http://software.incf.org/software/nineml). Although these languages enable software interoperability and therefore model reuse and reproducibility, they lack semantic information that would facilitate efficient model sharing and retrieval. In the context of the INCF Multi-Scale Modeling (MSM) program, we have developed an ontology to annotate spiking network models described with NineML and other structured model description languages. Ontologies are formal models of knowledge in a particular domain and composed of classes that represent concepts defining the field as well as the logical relations that link these concepts together [3]. These classes and relations have unique identifiers and definitions that allow unambiguous annotation of digital resources such as web pages or model source code. Implemented in a machine-readable format, these knowledge models can be used to design more efficient and intuitive information retrieval systems for experts in the field. We are proposing the first version of the Computational Neuroscience Ontology or CNO. This ontology is composed of 207 classes representing general concepts related to computational neuroscience organized in a hierarchy of concepts. CNO is currently available on Bioportal (http://bioportal.bioontology.org/ontologies/3003). The design of CNO follows some of the recommendations of the Open Biological and Biomedical Ontologies (OBO) community and is compatible with the ontologies developed and maintained within the Neuroscience Information Framework (NIF, [4]http://www.neuinfo.org). Integration with this large federation of neuroscience ontologies has two main advantages: (1) it allows the linking of models with biological information, creating a bridge between computational and experimental knowledge bases; (2) as ontology development is an iterative process that relies on inputs from the community, NIF has developed NeuroLex (http://neurolex.org), an effective collaborative platform, available for community inputs on the content in CNO. With the further development of CNO based on inputs from the community, we hope CNO will provide a useful framework to federate digital resources in the field of computational neuroscience.

Merging Healthcare Ontologies: Inconsistency Tolerance and Implementation Issues

A major challenge for ontology integration is to deal with inconsistencies. Existing merging tools are based on classical logic and are forced to avoid inconsistencies (to prevent the logic from becoming explosive) which may cause valuable information loss. However, inconsistent information may serve as an integral component in healthcare systems to give a full clinical perspective: any information loss is undesirable. In this paper we discuss various implementation issues for the development of a prototype merging system which will provide an inconsistency-tolerant reasoning mechanism applicable to the healthcare domain.

The Six Pillars for Building Big Data Analytics Ecosystems

With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting-with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes. Existing solutions cover bits-and-pieces of the analytics process, leaving it to organizations to assemble their own ecosystem or buy an off-the-shelf ecosystem that can have unnecessary components to them. We build on this point by dividing the Big Data Analytics problem into six main pillars. We characterize and show examples of solutions designed for each of these pillars. We then integrate these six pillars into a taxonomy to provide an overview of the possible state-of-the-art analytics ecosystems. In the process, we highlight a number of ecosystems to meet organizations different needs. Finally, we identify possible areas of research for building future Big Data Analytics Ecosystems.

A knowledge based approach to matching human neurodegenerative disease and animal models

Neurodegenerative diseases present a wide and complex range of biological and clinical features. Animal models are key to translational research, yet typically only exhibit a subset of disease features rather than being precise replicas of the disease. Consequently, connecting animal to human conditions using direct data-mining strategies has proven challenging, particularly for diseases of the nervous system, with its complicated anatomy and physiology. To address this challenge we have explored the use of ontologies to create formal descriptions of structural phenotypes across scales that are machine processable and amenable to logical inference. As proof of concept, we built a Neurodegenerative Disease Phenotype Ontology (NDPO) and an associated Phenotype Knowledge Base (PKB) using an entity-quality model that incorporates descriptions for both human disease phenotypes and those of animal models. Entities are drawn from community ontologies made available through the Neuroscience Information Framework (NIF) and qualities are drawn from the Phenotype and Trait Ontology (PATO). We generated ~1200 structured phenotype statements describing structural alterations at the subcellular, cellular and gross anatomical levels observed in 11 human neurodegenerative conditions and associated animal models. PhenoSim, an open source tool for comparing phenotypes, was used to issue a series of competency questions to compare individual phenotypes among organisms and to determine which animal models recapitulate phenotypic aspects of the human disease in aggregate. Overall, the system was able to use relationships within the ontology to bridge phenotypes across scales, returning non-trivial matches based on common subsumers that were meaningful to a neuroscientist with an advanced knowledge of neuroanatomy. The system can be used both to compare individual phenotypes and also phenotypes in aggregate. This proof of concept suggests that expressing complex phenotypes using formal ontologies provides considerable benefit for comparing phenotypes across scales and species.

Ontology Merging and Reasoning Using Paraconsistent Logics

Dealing with the inconsistencies that might arise during the ontology merging process constitutes a major challenge. The explosive nature of classical logic requires any logic-based merging effort to dissolve possible contradictions, and thus maintain consistency. In many cases, however, inconsistent information may be useful for intelligent reasoning activities. In healthcare systems, for example, inconsistent information may be required to provide a full clinical perspective, and thus any information loss is undesirable. The authors present a 4-valued logic-based merging system that exhibits inconsistency-tolerant behavior to avoid information loss.

Integrating Healthcare Ontologies: Inconsistency Tolerance and Case Study

A major challenge for ontology integration is to effectively deal with inconsistencies that arise during the merging process. Because of the explosive nature of classical logic, the common strategy in existing merging tools is to choose between the contradictory pieces of information and maintain consistency. In many cases inconsistent information may be useful for intelligent reasoning activities. For example, in healthcare systems inconsistent information may be required to provide a full clinical perspective so any information loss is undesirable. In this paper we present a multi-valued logic based merging system that has inconsistency tolerant behavior and avoids information loss. As an application of the system in the healthcare domain, a result of merging a subset of two healthcare ontologies SNOMED CT and ICNP is presented.