Document Detail

A Comprehensive Comparison of Association Estimators for Gene Network Inference Algorithms.
MedLine Citation:
PMID:  24728859     Owner:  NLM     Status:  Publisher    
MOTIVATION: Gene Network Inference (GNI) algorithms enable the researchers to explore the interactions among the genes and gene products by revealing these interactions. The principal process of the GNI algorithms is to obtain the association scores among genes. Although there are several association estimators utilized in different applications, there is no commonly accepted estimator as the best one for the GNI applications. In this study, 27 different interaction estimators were reviewed and 14 most promising ones among them are evaluated by using three popular GNI algorithms with two synthetic and two real biological datasets belonging to Escherichia coli (E. coli) bacteria and Saccharomyces cerevisiae (S.cerevisiae) yeast. Influences of the Copula Transform (CT) pre-processing operation on the performance of the interaction estimators are also observed. This study is expected to assist many researchers while studying with GNI applications.
RESULTS: B-spline, Pearson-based Gaussian and Spearman-based Gaussian association score estimators outperform the others for all datasets in terms of the performance and runtime. In addition to this, it is observed that, when the CT operation is used inference performances of the estimators mostly increase, especially for two synthetic datasets. Detailed evaluations and discussions are given in the experimental results.
Zeyneb Kurt; Nizamettin Aydin; Gökmen Altay
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-4-11
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  -     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2014 Apr 
Date Detail:
Created Date:  2014-4-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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