A data mining based approach for collaborative analysis of biomedical data

International Journal on Artificial Intelligence Tools

Volumn 23, Issue 4, 2014, Pages

  Tsiliki, Georgia ^a   Kossida, Sophia ^a   Friesen, Natalja ^b   Rüping, Stefan ^b   Tzagarakis, Manolis ^c   Karacapilidis, Nikos ^c  

^a BIOMEDICAL RESEARCH FOUNDATION   (Greece)

^b FRAUNHOFER IAIS   (Germany)

^c University of Patras and Computer Technology Institute   (Greece)

Author keywords

collaboration; data mining; Decision making; evaluation

Indexed keywords

ALGORITHMS; DECISION MAKING;

BIOMEDICAL RESEARCH; COLLABORATION; COLLABORATIVE ANALYSIS; COLLABORATIVE DECISION MAKING; DATA MINING ALGORITHM; EVALUATION; MULTI-DISCIPLINARY COLLABORATIONS; RESEARCH OBJECTIVES;

DATA MINING;

EID: 84906841491     PISSN: 02182130     EISSN: 17936349     Source Type: Journal    
DOI: 10.1142/S0218213014600100     Document Type: Article

References (27)

1. M. D. Brazas, D. Michelle, J. T. T Yamada, and B. F. Ouellette, Evolution in bioinformatic resources: 2009 update on the Bioinformatics Links Directory, Nucleic Acids Res. 37 (2009) W3-5.
2. N. Hara, P. Solomon, S.-L. Kim, and D. H. Sonnenwald, An emerging view of scientific collaboration: Scientists' perspectives on collaboration and factors that impact collaboration, Wiley Subscription Services, Inc., A Wiley Company 54 (2003) 952-965.
3. E. S. Lee, Facilitating collaborative biomedical research, GROUP 2007 Doctoral Consortium papers, (ACM, NY, USA, 2007), pp. 5:1-5:2.
4. D. E. Sullivan, J. L. Gabbard, M. Shukla and B. Sobral, Data integration for dynamic and sustainable systems biology resources: Challenges and lessons learned, Chem Biodiversity 7 (2010) 1124-1141.
5. C. Stark, B. J. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, and M. Tyers, BioGRID: A general repository for interaction datasets, Nucleic Acids Res. 34 (2006) D535-D539.
6. A. Birkl and G. Yona, Biozon: A hub of heterogeneous biological data, Nucleic Acids Res. 34 (2006) D235-D244.
7. J. M. Guberman et al., BioMart Central Portal: An open database network for the biological community, Database (2011) bar041.
8. C. A. Goble, J. Bhagat, S. Aleksejevs, D. Cruickshank, D. Michaelides, D. Newman, M. Borkum, S. Bechhofer, M. Roos, P. Li and D. De Roure, myExperiment: A repository and social network for the sharing of bioinformatics workflows, Nucleic Acids Res. 38 (2006) W677-W682.
9. J. Bhagat, F. Tanoh, E. Nzuobontane, T. Laurent, J. Orlowski, M. Roos, K. Wolstencroft, S. Aleksejevs, R. Stevens, S. Pettifer, R. Lopez, and C. A. Goble, BioCatalogue: A universal catalogue of web services for the life sciences, Nucleic Acids Res. 38 (2010) W689-W694.
10. L. J. van't Veer, H. Dai, M. J. van de Vijver, Y. D. He, A. A. M. Hart, M. Mao, H. L. Peterse, K. van der Kooy, M. J. Marton, A. T. Witteveen, G. J. Schreiber, R.M. Kerkhoven, C. Roberts, P. S. Linsley, R. Bernards, and S. H. Friend, Gene expression profiling predicts clinical outcome of breast cancer, Nature 415(6871) (2002) 530-536.
11. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, and C. D. Bloomfield, Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science 286 (1999) 531-537.
12. T. P. Speed, Statistical analysis of gene expression microarray data, Interdisciplinary Statistics (Chapman & Hall/CRC, 2005).
13. D. Chaussabel and A. Sher, Mining microarray expression data by literature profiling, Genome Biol. 3(10) (2002) 0055.1-0055.16.
14. A. Faro, D. Giordano, and C. Spampinato, Combining literature text mining with microarray data: advances for system biology modeling, Brief Bioinf. 13 (1) (2012) 61-82.
15. M. Kanehisa and S. Goto, KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acid Res. 28(1) (1999) 27-30.
16. B. Kavsek, N. Lavrac, and V. Jovanoski, APRIORI-SD: Adapting association rule learning to subgroup discovery, in Proc. 5th Int. Symp. on Intelligent Data Analysis (2003), pp. 230-241.
17. H. Grosskreutz, S. Rüping, and S. Wrobel, Tight optimistic estimates for fast subgroup discovery, in Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (Springer, 2008), pp. 440-456.
18. N. Lavrac, B. Cestnik, D. Gamberger, and P. A. Flach, Decision support through subgroup discovery: Three case studies and the lessons learned, Machine Learning 57(1-2) (2004) 115-143.
19. S. A. Amundson, K. T. Do, L. C. Vinikoor, R. A. Lee, C. A. Koch-Paiz, J. Ahn, M. Reimers, Y. Chen, D. A. Scudiero, J. N. Weinstein, J. M. Trent, M. L. Bittner, P. S. Meltzer, and A. J. Fornace, Integrating global gene expression and radiation survival parameters across the 60 cell lines of the national cancer institute anticancer drug screen, Cancer Res. 68(2) (2008) 415-424.
20. F. M. Shipman and R. McCall, Supporting knowledge-base evolution with incremental formalization, in Proc. CHI 1994 Conf. (1994), pp. 285-291.
21. C. Marshall and F. Shipman, Spatial hypertext and the practice of information triage, in Proc. of the 8th ACM Conf. on Hypertext (Southampton, UK, 1997), pp. 124-133.
22. W. Kunz and H. Ritte, Issues as Elements of Information Systems, Technical Report 0131, Universität Stuttgart, Institut für Grundlagen der Planning, 1970.
23. A. S. Jadhav and R. M. Sonar, Evaluating and selecting software packages: A review, Inform Software Tech. 51 (2009) 555-563.
24. Y. Sun and S. Greenberg, Places for lightweight group meetings: The design of come together, in Proc. on 16th ACM Int. Conf. on Supporting Group Work, GROUP 2010 (2010), pp. 135-244.
25. J. Cheng and D. Cosley, Kultagg: Lucid design for tagging interfaces, in Proc. on 16th ACM Int. Conf. on Supporting Group Work, GROUP 2010 (2010), pp. 169-172.
26. B. Kitchenham and S. L. Pfleeger, Principles of survey research part 5: Populations and samples, Software Engeneering Notes 27(5) (2002) 17-20.
27. D. A. Norman, The Design of Everyday Things, 2nd edn. (The MIT Press, 1998).