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Title:A novel weighted rank aggregation algorithm with applications in gene prioritization
Author(s):Raisali, Fardad
Advisor(s):Milenkovic, Olgica
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(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.
Issue Date:2017-07-19
Type:Thesis
URI:http://hdl.handle.net/2142/98424
Rights Information:Copyright 2017 Fardad Raisali
Date Available in IDEALS:2017-09-29
Date Deposited:2017-08


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