The Community for Technology Leaders
RSS Icon
Issue No.06 - Nov.-Dec. (2012 vol.16)
pp: 68-71
M. Dumontier , Dept. of Biol., Carleton Univ., Ottawa, ON, Canada
Drug discovery presents many challenges, but several linked data initiatives are under way to address the huge increase in the amount of data available from chemistry, biology, and drug discovery in the past two decades. This Web extra provides a list of relevant resources for linked data in drug discovery.
medical computing, data handling, drugs, biology, linked data, drug discovery, chemistry, Semantic Web, Life sciences, Drugs, Knowledge discovery, life sciences, medical computing, data handling, drugs, biology, linked data, drug discovery, chemistry, Semantic Web, Life sciences, Drugs, Knowledge discovery, innovation, drug discovery, linked data, semantic web
M. Dumontier, D. J. Wild, "Linked Data in Drug Discovery", IEEE Internet Computing, vol.16, no. 6, pp. 68-71, Nov.-Dec. 2012, doi:10.1109/MIC.2012.122
1. C.P Austin et al., “NIH Molecular Libraries Initiative,” Science, 2004, vol. 306, no. 5699, 2004, pp. 1138–1139.
2. T.I. Oprea et al., “Systems Chemical Biology,” Nat'l Chemical Biology, vol. 3, 2007, pp. 447–450.
3. D.J. Wild et al., “Systems Chemical Biology and the Semantic Web: What They Mean for the Future of Drug Discovery Research,” Drug Discovery Today, vol. 17, 2012, pp. 469–474.
4. T. Slater, C. Bouton, and E.S. Huang, “Beyond Data Integration,” Drug Discovery Today, vol. 13, nos. 13–14, 2008, pp. 584–589.
5. Q. Zhu et al., “WENDI: A Tool for Finding Nonobvious Relationships between Compounds and Biological Properties, Genes, Diseases and Scholarly Publications,” J. Cheminformatics, vol. 2, 2010, p. 6.
6. A. Ruttenberg et al., “Advancing Translational Research with the Semantic Web,” BMC Bioinformatics, vol. 8, supplement 3, 2007, S2.
7. M.A. Nolin et al., “Building an HIV Data Mashup using Bio2RDF,” Brief Bioinformatics, vol. 13, no. 1, 2012, pp. 98–106.
8. F. Belleaud et al., “Bio2RDF: Towards a Mashup to Build Bioinformatics Knowledge Systems,” J. Biomedical Informatics, vol. 41, no. 5, 2008, pp. 706–716.
9. B. Chen et al., “Chem2Bio2RDF: A Semantic Framework for Linking and Data Mining Chemogenomic and Systems Chemical Biology Data, BMC Bioinformatics, vol. 11, 2010, p. 255.
10. B. He et al., “Mining Association Paths in Relational Biomedical Data,” PloS One, vol. 6, no. 12, 2011, e27506.
11. B. Chen, Y. Ding, and D.J. Wild, “Assessing Drug Target Association using Semantic Linked Data,” PLoS Computational Biology, vol. 8, no. 7, 2012, e1002574.
12. J.S. Luciano et al., “The Translational Medicine Ontology and Knowledge Base: Driving Personalized Medicine by Bridging the Gap between Bench and Bedside,” J. Biomedical Semantics, vol. 2, supplement 2, 2007, S1.
13. A. Oellrich et al., “Improving Disease Gene Prioritization by Comparing the Semantic Similarity of Phenotypes in Mice with Those of Human Diseases, PLoS One, vol. 7, no. 6, 2012, e38937.
14. R. Hoehndorf, M. Dumontier, and G.V. Gkoutos, “Identifying Aberrant Pathways through Integrated Analysis of Knowledge in Pharmacogenomics,” Bioinformatics, vol. 28, no. 16, 2012, pp. 2169–2175.
15. N. Juty, N. Le Novère, and C. Laibe, “ and MIRIAM Registry: Community Resources to Provide Persistent Identification,” Nucleic Acids Research, vol. 40, Jan. 2012, pp. D580–586.
16. G.P. Patrinos et al., “Microattribution and Nanopublication as Means to Incentivize the Placement of Human Genome Variation Data into the Public Domain,” Human Mutation,26 June 2012.
17. M. Samwald et al., “Semantically Enabling Pharmacogenomic Data for the Realization of Personalized Medicine,” Pharmacogenomics, vol. 13, no. 2, 2012, pp. 201–212.
145 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool