Missing value imputation for microarray gene expression data using histone acetylation information.

ABSTRACT:

Background

It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.

Results

The paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(k-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental

SUBMITTER: Xiang Q 

PROVIDER: S-EPMC2432074 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature