6533b7d3fe1ef96bd1261617

RESEARCH PRODUCT

Missing Value Estimation for Microarray Data by Bayesian Principal Component Analysis and Iterative Local Least Squares

Dan ZhangJun ChenHamid Reza KarimiFuxi Shi

subject

EstimationVDP::Mathematics and natural science: 400::Mathematics: 410::Applied mathematics: 413Article SubjectComputer sciencelcsh:MathematicsGeneral MathematicsGeneral EngineeringValue (computer science)lcsh:QA1-939Non-linear iterative partial least squarescomputer.software_genreLeast squaresBayesian principal component analysislcsh:TA1-2040Data mininglcsh:Engineering (General). Civil engineering (General)computer

description

Published version of an article from the journal: Mathematical Problems in Engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2013/162938 Missing values are prevalent in microarray data, they course negative influence on downstream microarray analyses, and thus they should be estimated from known values. We propose a BPCA-iLLS method, which is an integration of two commonly used missing value estimation methods-Bayesian principal component analysis (BPCA) and local least squares (LLS). The inferior row-average procedure in LLS is replaced with BPCA, and the least squares method is put into an iterative framework. Comparative result shows that the proposed method has obtained the highest estimation accuracy across all missing rates on different types of testing datasets.

yearjournalcountryeditionlanguage
2013-01-01Mathematical Problems in Engineering
10.1155/2013/162938http://dx.doi.org/10.1155/2013/162938