Sehgal, Shoaib; Gondal, Iqbal and Dooley, Laurence S.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1007/11589990_30|
|Google Scholar:||Look up in Google Scholar|
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 of Squares and Weighted Partial Least Squares where Ridge Partial Least Square algorithm is used as a class predictor.
|Item Type:||Book Chapter|
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Laurence Dooley|
|Date Deposited:||10 Apr 2008|
|Last Modified:||14 Jan 2016 16:54|
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