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Title:Gene prioritization through hybrid distance-score rank aggregation
Author(s):Kim, Minji
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Gene prioritization
Rank Aggregation
Abstract:This thesis is concerned with developing novel rank aggregation methods for gene prioritization. Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to the training set, and the rankings of genes under various similarity criteria are aggregated via statistical methods. The contributions of our work are threefold: a) First, based on the realization that there is no unique way to define an optimal aggregate for rankings, we investigate the predictive quality of a number of new aggregation methods and known fusion techniques from machine learning and social choice theory. b) Second, we propose a new approach to genomic data aggregation, termed HyDRA (Hybrid Distance-score Rank Aggregation), which combines the advantages of score-based and combinatorial aggregation techniques. We also propose incorporating a new top-vs-bottom (TvB) weighting feature into the hybrid schemes. The TvB feature ensures that aggregates are more reliable at the top of the list, rather than at the bottom, since only top candidates are tested experimentally. Specifically, we combine score-based Borda and Kendall permutation distance aggregation methods with TvB weightings. c) Third, we propose an iterative procedure for gene discovery that operates via successful augmentation of the set of training genes by genes discovered in previous rounds, checked for consistency. We tested HyDRA on a number of gene sets, including Autism, Breast cancer, Colorectal cancer, Endometriosis, Ischaemic stroke, Leukemia, Lymphoma, and Osteoarthritis. Furthermore, we performed iterative gene discovery for Glioblastoma, Meningioma and Breast cancer, using a sequentially augmented list of training genes related to the Turcot syndrome, Li-Fraumeni condition and other diseases. The methods outperform state-of-the-art software tools such as ToppGene and Endeavour.
Issue Date:2015-04-30
Rights Information:Copyright 2015 Minji Kim
Date Available in IDEALS:2015-07-22
Date Deposited:May 2015

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