A novel weighted rank aggregation algorithm with applications in gene prioritization

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Raisali, Fardad

Contributors dc:contributor

• Milenkovic, Olgica

• Rank aggregation
• Linear programming
• Weighted Kendall

Statement dc:rights

• Copyright 2017 Fardad Raisali

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/98424

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/98424