Missing Microarray Data Estimation Based on Projection onto Convex Sets Method
MetadataShow full item record
DNA microarrays have gained widespread uses in biological studies. Missing values in a microarray experiment must be estimated before further analysis. In this paper, we propose a projection onto convex sets based algorithm to incorporate all a priori knowledge about missing values into the estimation process. Two convex sets applicable to all microarray datasets are constructed based on singular value decomposition (SVD). In addition, in the two most popular missing value estimation methods KNNimpute and SVDimpute, there is a trade-off whether to use a specific group of genes for the missing value estimation or to use all genes. Our algorithm can provide an optimal combination of these two strategies. Experiments show our algorithm can achieve a reduction of 16% to 20% error than the KNNimpute and SVDimpute methods.
Proceedings of the 17th International Conference on Pattern Recognition
© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.