Abstract
Microarrays have unique ability to probe thousands of genes at a time that makes it a useful tool for variety of applications, ranging from diagnosis to drug discovery. However, data generated by microarrays often contains multiple missing gene expressions that affect the subsequent analysis, as most of the times these missing values are ignored. In this paper we have analyzed how accurate estimation of missing values can lead to better subsequent gene selection and class prediction. Collateral Missing Values Estimation (CMVE), which demonstrates superior imputation performance compared to Bayesian Principal Component Analysis (BPCA) Impute, K-Nearest Neighbour (KNN) algorithm, when estimating missing values in the BRCA1, BRCA2 and Sporadic genetic mutation samples present in ovarian cancer by exploiting both local/global and positive/negative correlation values. CMVE also consistently outperforms, in terms of classification accuracies, BPCA, KNN and ZeroImpute techniques. The imputation is followed by gene selection using fusion of Between Group to within Group Sum ofSquares and Weighted Partial Least Squares where Ridge Partial Least Square algorithm is used as a class predictor.
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Sehgal, M.S.B., Gondal, I., Dooley, L. (2005). Collateral Missing Value Estimation: Robust Missing Value Estimation for Consequent Microarray Data Processing. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_30
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DOI: https://doi.org/10.1007/11589990_30
Publisher Name: Springer, Berlin, Heidelberg
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