Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Towards Knowledge Driven Decision Support for Personalized Home-based Self-management of Chronic Diseases

The use of ICT technologies to facilitate self-management for patients with chronic diseases attracts increasing attention in smart healthcare. Existing research has mainly focused on sensing and data processing technologies with little work on decision support mechanisms and systems. In this paper, we propose a home-based decision support system based on a wide range of assessment metrics from medical assessment, social and psychological evaluation to behaviour analysis to help self-manage rehabilitation and wellbeing in a personalized manner for different patients. This paper develops semantic models for describing patients, their conditions, medical and behavioural assessments and inference mechanisms for decision recommendations. The research is undertaken in the context of mobile user self-management for Spondyloarthritis (SpA) patients. A case scenario is used to demonstrate the application of the proposed approach, technologies and principles

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems

Health Improvement Path: Ontological Approach to Self-management Support in Personal Health Management Systems

Ontologies have been used for knowledge modeling and reasoning in healthcare domain (e.g., homecare, hospital clinical procedure, mHealth, etc.), but few in a context of self-management in healthcare with no sufficient reasoning rules to specify a systematic health management plan for an individual. In response to such needs, we aim to provide a generic ontology model for organizing the broad range of multidisciplinary knowledge required in personal health management by applying the ontology design patterns as well as for being extensible to more specific activity ontologies (e.g., physical exercises, diet, medication intake, etc.). The scope of a proposed ontology is to classify core concepts and relations in health self-management process and to build axioms for health improvement plans to meet an individual’s needs and health capability/maturity level. The proposed ontology is developed based on our previous work, health capability maturity model (HCMM) and can be integrated with existing health-related ontologies for further specification in health management processes

Representation of clinical practice guideline components in OWL

Serie : Advances in intelligent systems and computing, ISSN 2194-5357, vol. 221The main purpose to attain with the advent of clinical decision sup-port systems is either to improve the quality of patient care or to reduce the oc-currence of clinical malpractice, such as medical errors and defensive medicine. It is therefore necessary a machine-readable support to integrate the recommen-dations of Clinical Practice Guidelines in such systems. CompGuide is a Com-puter-Interpretable Guideline model developed under Ontology Web Language that offers support for administrative information concerning a guideline, work-flow procedures, and the definition of clinical and temporal constraints. When compared to other models of the same type, besides having a comprehensive task network model, it introduces new temporal representations and the possi-bility of reusing pre-existing knowledge and integrating it in a guideline.(undefined

Reply to: Soils need to be considered when assessing the impacts of land-use change on carbon sequestration

Industrial Ecolog

Alignment of vaccine codes using an ontology of vaccine descriptions

BACKGROUND: Vaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines. METHODS: We designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology of Vaccine Descriptions (VaccO), with a dictionary for the analysis of multilingual vaccine descriptions. We implemented five algorithms for the alignment of vaccine coding systems, i.e., the identification of corresponding codes from different coding ystems, based on an analysis of the code descriptors. The algorithms were evaluated by comparing their results with manually created alignments in two reference sets including clinical and database-specific coding systems with multilingual code descriptors. RESULTS: The best-performing algorithm represented code descriptors as logical statements about entities in the VaccO ontology and used an ontology reasoner to infer common properties and identify corresponding vaccine codes. The evaluation demonstrated excellent performance of the approach (F-scores 0.91 and 0.96). CONCLUSION: The VaccO ontology allows the identification, representation, and comparison of heterogeneous descriptions of vaccines. The automatic alignment of vaccine coding systems can accelerate the readiness of EHR databases in collaborative vaccine studies

BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results

<p>Abstract</p> <p>Background</p> <p>High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.</p> <p>Results</p> <p>We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal <url>http://bioportal.bioontology.org/ontologies/44531</url>.</p> <p>Conclusions</p> <p>After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.</p