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  • 1
    Online Resource
    Online Resource
    InContext: curation of medical context for drug indications
    Springer Science and Business Media LLC ; 2021
    In:  Journal of Biomedical Semantics Vol. 12, No. 1 ( 2021-12)
    Details
    In: Journal of Biomedical Semantics, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2021-12)
    Abstract: Accurate and precise information about the therapeutic uses (indications) of a drug is essential for applications in drug repurposing and precision medicine. Leading online drug resources such as DrugCentral and DrugBank provide rich information about various properties of drugs, including their indications. However, because indications in such databases are often partly automatically mined, some may prove to be inaccurate or imprecise. Particularly challenging for text mining methods is the task of distinguishing between general disease mentions in drug product labels and actual indications for the drug. For this, the qualifying medical context of the disease mentions in the text should be studied. Some examples include contraindications, co-prescribed drugs and target patient qualifications. No existing indication curation efforts attempt to capture such information in a precise way. Here we fill this gap by presenting a novel curation protocol for extracting indications and machine processable annotations of contextual information about the therapeutic use of a drug. We implemented the protocol on a reference set of FDA-approved drug product labels on the DailyMed website to curate indications for 150 anti-cancer and cardiovascular drugs. The resulting corpus - InContext - focuses on anti-cancer and cardiovascular drugs because of the heightened societal interest in cancer and heart disease. In order to understand how InContext relates with existing reputable drug indication databases, we analysed it’s overlap with a state-of-the-art indications database - LabeledIn - as well as a reputable online drug compendium - DrugCentral. We found that 40% of indications sampled from DrugCentral (and 23% from LabeledIn) respectively, could not be accounted for in InContext. This raises questions about the veracity of indications not appearing in InContext. The additional contextual information curated by InContext about disease mentions in drug SPLs provides a foundation for more precise, structured and formal representations of knowledge related to drug therapeutic use, in order to increase accuracy and agreement of drug indication extraction methods for in silico drug repurposing.
    Type of Medium: Online Resource
    ISSN: 2041-1480
    URL: Article
    DOI: 10.1186/s13326-021-00234-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2548651-2
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  • 2
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    Online Resource
    Principles of KLM-style Defeasible Description Logics
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Computational Logic Vol. 22, No. 1 ( 2021-01-31), p. 1-46
    Details
    In: ACM Transactions on Computational Logic, Association for Computing Machinery (ACM), Vol. 22, No. 1 ( 2021-01-31), p. 1-46
    Abstract: The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition. We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC .
    Type of Medium: Online Resource
    ISSN: 1529-3785 , 1557-945X
    URL: Article
    DOI: 10.1145/3420258
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2025647-4
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  • 3
    Online Resource
    Online Resource
    The Case for a Linked Data Research Engine for Legal Scholars
    Cambridge University Press (CUP) ; 2020
    In:  European Journal of Risk Regulation Vol. 11, No. 1 ( 2020-03), p. 70-93
    Details
    In: European Journal of Risk Regulation, Cambridge University Press (CUP), Vol. 11, No. 1 ( 2020-03), p. 70-93
    Abstract: This contribution explores the application of data science and artificial intelligence to legal research, more specifically an element that has not received much attention: the research infrastructure required to make such analysis possible. In recent years, EU law has become increasingly digitised and published in online databases such as EUR-Lex and HUDOC. However, the main barrier inhibiting legal scholars from analysing this information is lack of training in data analytics. Legal analytics software can mitigate this problem to an extent. However, current systems are dominated by the commercial sector. In addition, most systems focus on search of legal information but do not facilitate advanced visualisation and analytics. Finally, free to use systems that do provide such features are either too complex to use for general legal scholars, or are not rich enough in their analytics tools. In this paper, we motivate the case for building a software platform that addresses these limitations. Such software can provide a powerful platform for visualising and exploring connections and correlations in EU case law, helping to unravel the “DNA” behind EU legal systems. It will also serve to train researchers and students in schools and universities to analyse legal information using state-of-the-art methods in data science, without requiring technical proficiency in the underlying methods. We also suggest that the software should be powered by a data infrastructure and management paradigm following the seminal FAIR (Findable, Accessible, Interoperable and Reusable) principles .
    Type of Medium: Online Resource
    ISSN: 1867-299X , 2190-8249
    URL: Article
    DOI: 10.1017/err.2019.51
    Language: English
    Publisher: Cambridge University Press (CUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2600871-3
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