Using Medians to Generate Consensus Rankings for Biological Data

详细信息    查看全文

• 作者：Sarah Cohen-Boulakia (12) cohen@lri.fr
  Alain Denise (123) denise@lri.fr
  Sylvie Hamel (4) sylvie.hamel@umontreal.ca
• 刊名：Lecture Notes in Computer Science
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：6809
• 期：1
• 页码：73-90
• 全文大小：262.8 KB
• 参考文献：1. Ailon, N.: Aggregation of Partial Rankings, p-Ratings and Top-m Lists. Algorithmica 57(2), 284–300 (2010)
  2. Ailon, N., Charikar, M., Newman, N.: Aggregating inconsistent information: Ranking and clustering. In: Proceedings of the 37th STOC, pp. 684–693 (2005)
  3. Betzler, N., Fellows, M.R., Guo, J., Niedermeier, R., Rosamond, F.A.: Fixed-Parameter Algorithms for Kemeny Scores. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 60–71. Springer, Heidelberg (2008)
  4. Brancotte, B., Biton, A., Bernard-Pierrot, I., Radvanyi, F., Reyal, F., Cohen-Boulakia, S.: Gene List significance at-a-glance with GeneValorization. To Appear in Bioinformatics (Application Note) (February 2011)
  5. Birkland, A., Yona, G.: Biozon: a system for unification, management and analysis of heterogeneous biological data. BMC Bioinformatics 7, 70 (2006)
  6. Blin, G., Crochemore, M., Hamel, S., Vialette, S.: Medians of an odd number of permutations. To Appear in Pure Mathematics and Applications (2011)
  7. Cohen-Boulakia, S., Biton, O., Davidson, S., Froidevaux, C.: BioGuideSRS: Querying Multiple Sources with a user-centric perspective. Bioinformatics 23(10), 1301–1303 (2006)
  8. Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank Aggregation Methods for the Web. In: Proceedings of the 10th WWW, pp. 613–622 (2001)
  9. Fagin, R., Kumar, R., Mahdian, M., Sivakumar, D., Vee, E.: Comparing and Aggregating Rankings with Ties. In: Proceedings of PODS 2004, pp. 47–55 (2004)
  10. Hussels, P., Trissl, S., Leser, U.: What’s new? What’s certain? – scoring search results in the presence of overlapping data sources. In: Cohen-Boulakia, S., Tannen, V. (eds.) DILS 2007. LNCS (LNBI), vol. 4544, pp. 231–246. Springer, Heidelberg (2007)
  11. Kendall, M.: A New Measure of Rank Correlation. Biometrika 30, 81–89 (1938)
  12. Kenyon-Mathieu, C., Schudy, W.: How to rank with few errors. In: Proceedings of the 39th STOC, pp. 95–103 (2007)
  13. Lacroix, Z., Raschid, L., Vidal, M.E.: Efficient techniques to explore and rank paths in life science data sources. In: Rahm, E. (ed.) DILS 2004. LNCS (LNBI), vol. 2994, pp. 187–202. Springer, Heidelberg (2004)
  14. Laignel, N.: Ranking biological data taking into account user’s preferences, Master thesis report (co-supervised by S. Cohen-Boulakia, C. Froidevaux, and U. Leser), University of Paris-Sud XI (2010)
  15. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Stanford University, Stanford (1998)
  16. Shafer, P., Isganitis, T., Yona, G.: Hubs of knowledge: using the functional link structure in Biozon to mine for biologically significant entities. BMC Bioinformatics 7, 71 (2006)
  17. Varadarajan, R., Hritidis, V., Raschid, L., Vidal, M., Ibanez, L., Rodriguez-Drumond, H.: Flexible and Efficient Querying and Ranking on Hyperlinked Data Sources. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 553–564 (2009)
  18. Wilf, H.S.: Generatingfunctionology, p. 147. Academic Press, NY (1990)
  19. van Zuylen, A., Williamson, D.P.: Deterministic algorithms for rank aggregation and other ranking and clustering problems. In: Kaklamanis, C., Skutella, M. (eds.) WAOA 2007. LNCS, vol. 4927, pp. 260–273. Springer, Heidelberg (2008)
• 作者单位：1. LRI (Laboratoire de Recherche en Informatique), CNRS UMR 8623, Universit茅 Paris-Sud, France2. AMIB Group, INRIA Saclay Ile-de-France, France3. IGM (Institut de G茅n茅tique et de Microbiologie), CNRS UMR 8621, Universit茅 Paris-Sud, France4. DIRO (D茅partement d鈥橧nformatique et de Recherche Op茅rationnelle), Universit茅 de Montr茅al, QC, Canada
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Faced with the deluge of data available in biological databases, it becomes increasingly difficult for scientists to obtain reasonable sets of answers to their biological queries. A critical example appears in medicine, where physicians frequently need to get information about genes associated with a given disease. When they pose such queries to Web portals (e.g., Entrez NCBI) they usually get huge amounts of answers which are not ranked, making them very difficult to be exploited. In the last years, while several ranking approaches have been proposed, none of them is considered as the most promising.