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https://doi.org/10.21256/zhaw-25030| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Computational literature-based discovery for natural products research : current state and future prospects |
| Autor/-in: | Lardos, Andreas Aghaebrahimian, Ahmad Koroleva, Anna Sidorova, Julia Wolfram, Evelyn Anisimova, Maria Gil, Manuel |
| et. al: | No |
| DOI: | 10.3389/fbinf.2022.827207 10.21256/zhaw-25030 |
| Erschienen in: | Frontiers in Bioinformatics |
| Band(Heft): | 2 |
| Heft: | 827207 |
| Erscheinungsdatum: | 15-Mär-2022 |
| Verlag / Hrsg. Institution: | Frontiers Research Foundation |
| ISSN: | 2673-7647 |
| Sprache: | Englisch |
| Schlagwörter: | Literature-based discovery; Natural product; Text mining; Knowledge graph; Natural language processing; Swanson; Semantic integration; Ontology |
| Fachgebiet (DDC): | 000: Allgemeines und Wissenschaft 006: Spezielle Computerverfahren |
| Zusammenfassung: | Literature-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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/25030 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Chemie und Biotechnologie (ICBT) Institut für Computational Life Sciences (ICLS) |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2022_Lardos-etal_ComputationalLiteratureBasedDiscovery_FrontBioinform.pdf | 787.86 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Lardos, A., Aghaebrahimian, A., Koroleva, A., Sidorova, J., Wolfram, E., Anisimova, M., & Gil, M. (2022). Computational literature-based discovery for natural products research : current state and future prospects. Frontiers in Bioinformatics, 2(827207). https://doi.org/10.3389/fbinf.2022.827207
Lardos, A. et al. (2022) ‘Computational literature-based discovery for natural products research : current state and future prospects’, Frontiers in Bioinformatics, 2(827207). Available at: https://doi.org/10.3389/fbinf.2022.827207.
A. Lardos et al., “Computational literature-based discovery for natural products research : current state and future prospects,” Frontiers in Bioinformatics, vol. 2, no. 827207, Mar. 2022, doi: 10.3389/fbinf.2022.827207.
LARDOS, Andreas, Ahmad AGHAEBRAHIMIAN, Anna KOROLEVA, Julia SIDOROVA, Evelyn WOLFRAM, Maria ANISIMOVA und Manuel GIL, 2022. Computational literature-based discovery for natural products research : current state and future prospects. Frontiers in Bioinformatics. 15 März 2022. Bd. 2, Nr. 827207. DOI 10.3389/fbinf.2022.827207
Lardos, Andreas, Ahmad Aghaebrahimian, Anna Koroleva, Julia Sidorova, Evelyn Wolfram, Maria Anisimova, and Manuel Gil. 2022. “Computational Literature-Based Discovery for Natural Products Research : Current State and Future Prospects.” Frontiers in Bioinformatics 2 (827207). https://doi.org/10.3389/fbinf.2022.827207.
Lardos, Andreas, et al. “Computational Literature-Based Discovery for Natural Products Research : Current State and Future Prospects.” Frontiers in Bioinformatics, vol. 2, no. 827207, Mar. 2022, https://doi.org/10.3389/fbinf.2022.827207.
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