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HyDRA: Gene Prioritization via Hybrid Distance-Score Rank Aggregation.
MedLine Citation:
PMID:  25411330     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
MOTIVATION: Fundamental results from social choice theory, political and computer sciences, and statistics have shown that there exists no consistent, fair and unique way to aggregate rankings. Instead, one has to decide on an aggregation approach using predefined set of desirable properties for the aggregate. The aggregation methods fall into two categories, score-based and distance-based approaches, each of which has its own drawbacks and advantages. This work is motivated by the observation that merging these two techniques in a computationally efficient manner, and by incorporating additional constraints, one can ensure that the predictive quality of the resulting aggregation algorithm is very high.
RESULTS: We tested HyDRA on a number of gene sets, including Autism, Breast cancer, Colorectal cancer, Endometriosis, Ischeemic 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. Despite this finding, we recommend as best practice to take the union of top-ranked items produced by different methods for the final aggregated list. Availability: The HyDRA software may be downloaded from: https://dl.dropboxusercontent.com/u/40200227/HyDRAsoftware.zip CONTACT: mkim158@illinois.edu.
Authors:
Minji Kim; Farzad Farnoud; Olgica Milenkovic
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-11-18
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  -     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2014 Nov 
Date Detail:
Created Date:  2014-11-20     Completed Date:  -     Revised Date:  2014-11-21    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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