1. Biomedical innovation at the laboratory, clinical and commercial interface. Mapping research grants, publications and patents in the field of microarrays Andrei Mogoutov, Alberto Cambrosio, Peter Keating & Philippe Mustar 6 th Biennial International Triple Helix Conference on University-Industry Government-Links Singapore, May 16-18, 2007

2. Main goals of this paper: To analyze biomedical innovation by triangulating three sources of information: publications, patents and research projects (see Verganti et al.) In particular: to develop a methodology for linking publication, patent and project databases by using emergent (rather than pre-established) categories Methods: Heterogeneous network analysis (ReseauLu X2) Text-mining (SPSS LexiQuest Mine)

3. Case study: Microarrays A DNA microarray (a.k.a as biochip, DNA chip, gene array, etc.) is a collection of microscopic DNA spots, commonly representing single genes, arrayed on a solid surface by covalent attachment to chemically suitable matrices Compared to previous molecular genetic approaches, a microarray experiment involves the simultaneous analysis of many hundreds or thousands of genes, as opposed to single ones Microarrays have become a key technology of the (post)genomic era Annual compounded growth rate of the microarray market between 1999-2004: 63%

4. Databases Publications: PubMed: robust keyword system; biomedical Web of Science: addresses and citations; general S&T [PubMed/WoS intersection] Research Projects: CRISP: NIH-financed projects; biomedical [NSF] Patents: Derwent Innovation Index [USPTO]/ [EUPTO]

5. 1. Characterizing the field of microarrays

6. Publications (PubMed)

7. Publications (Web of Science)

8. Publications: most cited authors

9. Mapping: ReseauLu X2

10. Co-authorship network (most cited authors)

11. Collaborative institutional network

12. Institutional network (4 nearest nodes) biotech company regulatory agency hospital university

13. Journal inter-citation network (5 nearest nodes) cancer cluster

14. Patents (Derwent)

15. CRISP Projects

16. 2. Database bridges 2a. Via authors and pre-established (institutional) categories

17. CRISP projects by Institute

18. CRISP projects vs. Publications Link via authors Categories by Institutes

19. CRISP projects vs. Citations Link via authors Categories by Institutes

20. CRISP projects vs. Patents Link via authors Categories by Institutes

21. 2. Database bridges 2b. Via content (emergent categories)

22. Text mining: SPSS LexiQuest Mine and Text Mining Builder Concept extraction Dictionary interface

23. Methodology for generating emergent categories The chosen database is text-mined (NLP software) to extract the relevant concepts (composite terms and uniterms): in the present case, WoS was chosen over CRISP because it includes biomedical and non-biomedical domains The most relevant (specific) concepts are selected by using a ChiSq filter After building a co-occurrence map (nearest nodes), clusters corresponding to sub- domains are identified by a modified fuzzy K- means clustering algorithm The list of concepts defining each sub- domain is used to analyze the other databases

24. Emergent sub-domains

25. Publications (WoS) by sub-domains

26. CRISP projects by sub- domains

27. Patents by sub-domains

28. % of sub-domains in projects, patents and publications

29. SNPs

30. Bioinformatics

31. Acknowledgments Research for this paper was supported by grants from: CIHR FQRSC SSHRC