A POCS-BASED METHOD FOR ESTIMATING UNOBSERVED VALUES IN MICROARRAYTIME-SERIES DATA
This paper presents a POCS-bascd (projection on convex set) method that estimates the unobserved time-points in microarray time-series data to make such data useful for clustering and aligning. Unobserved values are caused either by missing values or by unevenly sampling rates, and cannot be estimated accurately by straightforward interpolation due to very noisy and few replicated data. According to prior knowledge that each gene time-series is constrained in both time and frequency domains, POCS formulates these constraints by multiple convex sets and uses an itcratively convergent procedure to find the optimal value that satisfies all constraints by prior knowledge. To estimate the unobserved values, we use the cubic spline method to estimate the initial value and use POCS to find the optimal value iteratively. We show that POCS can improve the estimation of unobserved time-points with lower normalized root mean squared error compared with the statistical spline estimation for the continuous representation of microarray time-series data. Theoretically, the POCS-based method may improve the estimation performance further if more prior knowledge is available.
Projection on convez set (POCS) Missing values Unevenly sampling rates Microarray time-series data
JIA ZENG HONG YAN
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China School of Engin
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3898-3902
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）