Handling missing DNA microarray data by kriging estimators.
Microarray gene expression data provide life science researchers with much more sensitive and detailed information about gene expression patterns than conventional methodologies for the purpose of facilitating gene recognition efforts. However, due to insufficient image resolution and noise generated during microarray experiments, gene expression matrices are frequently represented with missing elements. Methods for estimating missing microarray data are therefore needed to allow further analysis. In this paper, we present two kriging estimators for estimating missing values in DNA microarrays. These approaches can be useful for downstream analysis of microarray-based gene expression data.
Pham
Tuan D
TD
Bioinformatics Applications Research Centre, School of Information Technology, James Cook University, Townsville, Queensland 4811, Australia.
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D046228:Microarray Analysis; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software