title: Autoregressive-model-based missing value estimation for DNA microarray time series data.
abstract: Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.
authors.affiliation: School of Electrical and Information Engineering, University of Sydney, N.S.W., Australia. miewkeen@ee.usyd.edu.au
authors.forename: Miew Keen
authors.initials: MK
authors.lastname: Choong
authors.forename: Maurice
authors.initials: M
authors.lastname: Charbit
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*