Literature DB >> 22823127

The use of web ontology languages and other semantic web tools in drug discovery.

Huajun Chen1, Guotong Xie.   

Abstract

IMPORTANCE OF THE FIELD: To optimize drug development processes, pharmaceutical companies require principled approaches to integrate disparate data on a unified infrastructure, such as the web. The semantic web, developed on the web technology, provides a common, open framework capable of harmonizing diversified resources to enable networked and collaborative drug discovery. AREAS COVERED IN THIS REVIEW: We survey the state of art of utilizing web ontologies and other semantic web technologies to interlink both data and people to support integrated drug discovery across domains and multiple disciplines. Particularly, the survey covers three major application categories including: i) semantic integration and open data linking; ii) semantic web service and scientific collaboration and iii) semantic data mining and integrative network analysis. WHAT THE READER WILL GAIN: The reader will gain: i) basic knowledge of the semantic web technologies; ii) an overview of the web ontology landscape for drug discovery and iii) a basic understanding of the values and benefits of utilizing the web ontologies in drug discovery. TAKE HOME MESSAGE: i) The semantic web enables a network effect for linking open data for integrated drug discovery; ii) The semantic web service technology can support instant ad hoc collaboration to improve pipeline productivity and iii) The semantic web encourages publishing data in a semantic way such as resource description framework attributes and thus helps move away from a reliance on pure textual content analysis toward more efficient semantic data mining.

Entities:  

Year:  2010        PMID: 22823127     DOI: 10.1517/17460441003762709

Source DB:  PubMed          Journal:  Expert Opin Drug Discov        ISSN: 1746-0441            Impact factor:   6.098


  5 in total

1.  The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

Authors:  Santiago Vilar; George Hripcsak
Journal:  Brief Bioinform       Date:  2017-07-01       Impact factor: 11.622

Review 2.  Empowering industrial research with shared biomedical vocabularies.

Authors:  Lee Harland; Christopher Larminie; Susanna-Assunta Sansone; Sorana Popa; M Scott Marshall; Michael Braxenthaler; Michael Cantor; Wendy Filsell; Mark J Forster; Enoch Huang; Andreas Matern; Mark Musen; Jasmin Saric; Ted Slater; Jabe Wilson; Nick Lynch; John Wise; Ian Dix
Journal:  Drug Discov Today       Date:  2011-09-23       Impact factor: 7.851

3.  Linked open drug data for pharmaceutical research and development.

Authors:  Matthias Samwald; Anja Jentzsch; Christopher Bouton; Claus Stie Kallesøe; Egon Willighagen; Janos Hajagos; M Scott Marshall; Eric Prud'hommeaux; Oktie Hassenzadeh; Elgar Pichler; Susie Stephens
Journal:  J Cheminform       Date:  2011-05-16       Impact factor: 5.514

4.  Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.

Authors:  Ozge Gurbuz; Gregorio Alanis-Lobato; Sergio Picart-Armada; Miao Sun; Christian Haslinger; Nathan Lawless; Francesc Fernandez-Albert
Journal:  Front Genet       Date:  2022-03-14       Impact factor: 4.599

5.  Cheminformatics and the Semantic Web: adding value with linked data and enhanced provenance.

Authors:  Jeremy G Frey; Colin L Bird
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2013-01-08
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.