An ensemble approach to microarray data-based gene prioritization after missing value imputation | Zendy

Hua Dong | Zendy; Yinglei Lai | Zendy

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An ensemble approach to microarray data-based gene prioritization after missing value imputation

Author(s) -

Hua Dong,

Yinglei Lai

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm010

Subject(s) - imputation (statistics) , missing data , computer science , data mining , false positive paradox , concordance , prioritization , sample size determination , statistics , mathematics , artificial intelligence , machine learning , bioinformatics , biology , engineering , management science

Microarrays have been widely used to discover novel disease related genes. Some types of microarray, such as cDNA arrays, usually contain a considerable portion of missing values. When missing value imputation and gene prioritization are sequentially conducted, it is necessary to consider the distribution space of prioritization scores due to the existence of missing values. We propose an ensemble approach to address this issue. A bootstrap procedure enables us to generate a resample multivariate distribution of the prioritization scores and then to obtain the expected prioritization scores.

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