A novel weighted rank aggregation algorithm with applications in gene prioritization
Raisali, Fardad
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https://hdl.handle.net/2142/98424
Description
Title
A novel weighted rank aggregation algorithm with applications in gene prioritization
Author(s)
Raisali, Fardad
Issue Date
2017-07-19
Director of Research (if dissertation) or Advisor (if thesis)
Milenkovic, Olgica
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Weighted Kendall
Rank aggregation
Linear programming
Abstract
We propose a new family of algorithms for bounding/approximating the optimal solution of rank aggregation problems based on weighted Kendall distances. The algorithms represent linear programming relaxations of integer programs that involve variables reflecting partial orders of three or more candidates. Our simulation results indicate that the linear programs give near-optimal performance for a number of important voting parameters, and outperform methods based on PageRank and Weighted Bipartite Matching. Finally, we illustrate the performance of the aggregation method on a set of test genes pertaining to the Bardet-Biedl syndrome, schizophrenia, and HIV and show that the combinatorial method matches or outperforms state-of-the art algorithms such as ToppGene.
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