iRDA: a new filter towards predictive, stable, and enriched candidate genes

BMC Genomics

Research Article

Hung-Ming Lai¹  Kathleen K. Steinhöfel¹  Andreas A. Albrecht² 

[1] Algorithms and Bioinformatics Research Group, Department of Informatics, King’s College London, Strand, WC2R 2LS, London, UK;School of Science and Technology, Middlesex University, Burroughs, NW4 4BT, London, UK;

关键词: Cancer phenotype prediction;    Feature selection and classification;    Microarray;    Prognosis gene signature;    Transcriptomic profiling;   

DOI  :  10.1186/s12864-015-2129-5

 received in 2015-04-09, accepted in 2015-10-22,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundGene expression profiling using high-throughput screening (HTS) technologies allows clinical researchers to find prognosis gene signatures that could better discriminate between different phenotypes and serve as potential biological markers in disease diagnoses. In recent years, many feature selection methods have been devised for finding such discriminative genes, and more recently information theoretic filters have also been introduced for capturing feature-to-class relevance and feature-to-feature correlations in microarray-based classification.MethodsIn this paper, we present and fully formulate a new multivariate filter, iRDA, for the discovery of HTS gene-expression candidate genes. The filter constitutes a four-step framework and includes feature relevance, feature redundancy, and feature interdependence in the context of feature-pairs. The method is based upon approximate Markov blankets, information theory, several heuristic search strategies with forward, backward and insertion phases, and the method is aiming at higher order gene interactions.ResultsTo show the strengths of iRDA, three performance measures, two evaluation schemes, two stability index sets, and the gene set enrichment analysis (GSEA) are all employed in our experimental studies. Its effectiveness has been validated by using seven well-known cancer gene-expression benchmarks and four other disease experiments, including a comparison to three popular information theoretic filters. In terms of classification performance, candidate genes selected by iRDA perform better than the sets discovered by the other three filters. Two stability measures indicate that iRDA is the most robust with the least variance. GSEA shows that iRDA produces more statistically enriched gene sets on five out of the six benchmark datasets.ConclusionsThrough the classification performance, the stability performance, and the enrichment analysis, iRDA is a promising filter to find predictive, stable, and enriched gene-expression candidate genes.

【 授权许可】

CC BY   
© Lai et al. 2015

【 图 表 】

12864_2015_2129_Article_IEq36.gif

12864_2017_4020_Article_IEq8.gif

12864_2015_1944_Article_IEq13.gif

12864_2015_2129_Article_IEq33.gif

12864_2017_3920_Article_IEq6.gif

12864_2015_2129_Article_IEq31.gif

12864_2015_2129_Article_IEq30.gif

12864_2017_3645_Article_IEq4.gif

12864_2017_4359_Article_IEq3.gif

12864_2017_3898_Article_IEq2.gif

12864_2015_1970_Article_IEq4.gif

12864_2017_3487_Article_IEq42.gif

12864_2017_3990_Article_IEq13.gif

12864_2017_3781_Article_IEq1.gif

12864_2017_3990_Article_IEq12.gif

12864_2017_3777_Article_IEq4.gif

12864_2015_2129_Article_IEq20.gif

12864_2016_3425_Article_IEq6.gif

12864_2015_2129_Article_IEq18.gif

12864_2017_3920_Article_IEq2.gif

12864_2017_3920_Article_IEq1.gif

12894_2016_184_Article_IEq3.gif

12864_2017_4071_Article_IEq2.gif

12894_2016_184_Article_IEq1.gif

12864_2015_2129_Article_IEq12.gif

12864_2015_2129_Article_IEq11.gif

12864_2015_2129_Article_IEq10.gif

12864_2015_2129_Article_IEq9.gif

12864_2015_2129_Article_IEq8.gif

12864_2015_2055_Article_IEq29.gif

12888_2016_877_Article_IEq19.gif

12864_2015_2129_Article_IEq5.gif

12864_2017_4030_Article_IEq32.gif

12864_2016_3425_Article_IEq1.gif

12864_2016_3440_Article_IEq67.gif

12888_2016_877_Article_IEq5.gif