Expertise Profiling in Evolving Knowledgecuration Platforms

Expertise modeling has been the subject of extensiveresearch in two main disciplines: Information Retrieval (IR) andSocial Network Analysis (SNA). Both IR and SNA approachesbuild the expertise model through a document-centric approachproviding a macro-perspective on the knowledge emerging fromlarge corpus of static documents. With the emergence of the Webof Data there has been a significant shift from static to evolvingdocuments, through micro-contributions. Thus, the existingmacro-perspective is no longer sufficient to track the evolution ofboth knowledge and expertise. In this paper we present acomprehensive, domain-agnostic model for expertise profiling inthe context of dynamic, living documents and evolving knowledgebases. We showcase its application in the biomedical domain andanalyze its performance using two manually created datasets

Mapping the gap: curation of phenotype-driven gene discovery in congenital heart disease research

The goal of translational research is to improve public health by accelerating basic science discovery to human application and clinical practice. The NHLBI Bench-to-Bassinet (B2B) program promotes this goal through its translational research initiative. Together with other collaborators of the B2B program, the University of Pittsburgh mutagenesis screen strives to elucidate the underlying genetic and developmental processes of congenital heart disease (CHD), which is a significant source of morbidity and mortality in the population. The screen investigators have curated over 200 mouse models of CHD on the Jackson Laboratory (JAX) Mouse Genome Database (MGD) through a multi-tiered strategy of phenotypic and genetic analyses. Within the translational research paradigm, this screen has contributed to the improvement of public health and patient care by enabling the identification of 107 pathogenic mutations in 68 unique genes as well as providing 62 models of human disease for future research and development of therapies. Two mutant mouse lines, lines 1702 and 2407, will be thoroughly discussed with regard to their significance to research. However, analysis of the screen curation protocol demonstrated inefficiencies representative of problems across the entirety of the translational research continuum. Within this continuum, data must be translated and readily shared between databases in each domain. Research is currently scattered across disconnected, autonomous databases, which prevents data integration and comprehensive retrieval of information from a single platform. Moreover, data are represented as a combination of discordant ontologies and free-text annotations, which further impede cross-species or cross-domain comparisons and database integration. Although ontology mapping endeavors have achieved some success, the process is flawed with unequivocal alignments or inaccuracies and requires extensive manual validation. Harmonization of ontologies through, ideally, a standardized, relational framework, is necessary to improve the efficacy and utility of translational research. In summary, the future progress of translational research, as exemplified by the University of Pittsburgh B2B program, and its potential in improving public health depends on the acceleration of basic discovery to clinical application through a network of integrated databases supported by a unified ontological system

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

The Human Phenotype Ontology in 2017.

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others