Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25030
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dc.contributor.authorLardos, Andreas-
dc.contributor.authorAghaebrahimian, Ahmad-
dc.contributor.authorKoroleva, Anna-
dc.contributor.authorSidorova, Julia-
dc.contributor.authorWolfram, Evelyn-
dc.contributor.authorAnisimova, Maria-
dc.contributor.authorGil, Manuel-
dc.date.accessioned2022-05-30T13:43:04Z-
dc.date.available2022-05-30T13:43:04Z-
dc.date.issued2022-03-15-
dc.identifier.issn2673-7647de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25030-
dc.description.abstractLiterature-based discovery (LBD) mines existing literature in order to generate new hypotheses by finding links between previously disconnected pieces of knowledge. Although automated LBD systems are becoming widespread and indispensable in a wide variety of knowledge domains, little has been done to introduce LBD to the field of natural products research. Despite growing knowledge in the natural product domain, most of the accumulated information is found in detached data pools. LBD can facilitate better contextualization and exploitation of this wealth of data, for example by formulating new hypotheses for natural product research, especially in the context of drug discovery and development. Moreover, automated LBD systems promise to accelerate the currently tedious and expensive process of lead identification, optimization, and development. Focusing on natural product research, we briefly reflect the development of automated LBD and summarize its methods and principal data sources. In a thorough review of published use cases of LBD in the biomedical domain, we highlight the immense potential of this data mining approach for natural product research, especially in context with drug discovery or repurposing, mode of action, as well as drug or substance interactions. Most of the 91 natural product-related discoveries in our sample of reported use cases of LBD were addressed at a computer science audience. Therefore, it is the wider goal of this review to introduce automated LBD to researchers who work with natural products and to facilitate the dialogue between this community and the developers of automated LBD systems.de_CH
dc.language.isoende_CH
dc.publisherFrontiers Research Foundationde_CH
dc.relation.ispartofFrontiers in Bioinformaticsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectLiterature-based discoveryde_CH
dc.subjectNatural productde_CH
dc.subjectText miningde_CH
dc.subjectKnowledge graphde_CH
dc.subjectNatural language processingde_CH
dc.subjectSwansonde_CH
dc.subjectSemantic integrationde_CH
dc.subjectOntologyde_CH
dc.subject.ddc000: Allgemeines und Wissenschaftde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleComputational literature-based discovery for natural products research : current state and future prospectsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Chemie und Biotechnologie (ICBT)de_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.3389/fbinf.2022.827207de_CH
dc.identifier.doi10.21256/zhaw-25030-
zhaw.funding.euNode_CH
zhaw.issue827207de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedComputational Genomicsde_CH
zhaw.webfeedHealth Research Hub (LSFM)de_CH
zhaw.webfeedPhytopharmaziede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.monitoring.costperiod2022de_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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