Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Окружење за анализу и оцену квалитета великих и повезаних података

Linking and publishing data in the Linked Open Data format increases the interoperability and discoverability of resources over the Web. To accomplish this, the process comprises several design decisions, based on the Linked Data principles that, on one hand, recommend to use standards for the representation and the access to data on the Web, and on the other hand to set hyperlinks between data from different sources. Despite the efforts of the World Wide Web Consortium (W3C), being the main international standards organization for the World Wide Web, there is no one tailored formula for publishing data as Linked Data. In addition, the quality of the published Linked Open Data (LOD) is a fundamental issue, and it is yet to be thoroughly managed and considered. In this doctoral thesis, the main objective is to design and implement a novel framework for selecting, analyzing, converting, interlinking, and publishing data from diverse sources, simultaneously paying great attention to quality assessment throughout all steps and modules of the framework. The goal is to examine whether and to what extent are the Semantic Web technologies applicable for merging data from different sources and enabling end-users to obtain additional information that was not available in individual datasets, in addition to the integration into the Semantic Web community space. Additionally, the Ph.D. thesis intends to validate the applicability of the process in the specific and demanding use case, i.e. for creating and publishing an Arabic Linked Drug Dataset, based on open drug datasets from selected Arabic countries and to discuss the quality issues observed in the linked data life-cycle. To that end, in this doctoral thesis, a Semantic Data Lake was established in the pharmaceutical domain that allows further integration and developing different business services on top of the integrated data sources. Through data representation in an open machine-readable format, the approach offers an optimum solution for information and data dissemination for building domain-specific applications, and to enrich and gain value from the original dataset. This thesis showcases how the pharmaceutical domain benefits from the evolving research trends for building competitive advantages. However, as it is elaborated in this thesis, a better understanding of the specifics of the Arabic language is required to extend linked data technologies utilization in targeted Arabic organizations.Повезивање и објављивање података у формату "Повезани отворени подаци" (енг. Linked Open Data) повећава интероперабилност и могућности за претраживање ресурса преко Web-а. Процес је заснован на Linked Data принципима (W3C, 2006) који са једне стране елаборира стандарде за представљање и приступ подацима на Wебу (RDF, OWL, SPARQL), а са друге стране, принципи сугеришу коришћење хипервеза између података из различитих извора. Упркос напорима W3C конзорцијума (W3C је главна међународна организација за стандарде за Web-у), не постоји јединствена формула за имплементацију процеса објављивање података у Linked Data формату. Узимајући у обзир да је квалитет објављених повезаних отворених података одлучујући за будући развој Web-а, у овој докторској дисертацији, главни циљ је (1) дизајн и имплементација иновативног оквира за избор, анализу, конверзију, међусобно повезивање и објављивање података из различитих извора и (2) анализа примена овог приступа у фармацeутском домену. Предложена докторска дисертација детаљно истражује питање квалитета великих и повезаних екосистема података (енг. Linked Data Ecosystems), узимајући у обзир могућност поновног коришћења отворених података. Рад је мотивисан потребом да се омогући истраживачима из арапских земаља да употребом семантичких веб технологија повежу своје податке са отвореним подацима, као нпр. DBpedia-јом. Циљ је да се испита да ли отворени подаци из Арапских земаља омогућавају крајњим корисницима да добију додатне информације које нису доступне у појединачним скуповима података, поред интеграције у семантички Wеб простор. Докторска дисертација предлаже методологију за развој апликације за рад са повезаним (Linked) подацима и имплементира софтверско решење које омогућује претраживање консолидованог скупа података о лековима из изабраних арапских земаља. Консолидовани скуп података је имплементиран у облику Семантичког језера података (енг. Semantic Data Lake). Ова теза показује како фармацеутска индустрија има користи од примене иновативних технологија и истраживачких трендова из области семантичких технологија. Међутим, како је елаборирано у овој тези, потребно је боље разумевање специфичности арапског језика за имплементацију Linked Data алата и њухову примену са подацима из Арапских земаља

Federal Register

Daily publication of the U.S. Office of the Federal Register contains rules and regulations, proposed legislation and rule changes, and other notices, including "Presidential proclamations and Executive Orders, Federal agency documents having general applicability and legal effect, documents required to be published by act of Congress, and other Federal agency documents of public interest" (p. ii). Table of Contents starts on page iii

A Model-Based Approach to Comprehensive Risk Management for Medical Devices

The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15].The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15]