An ensemble based missing value estimation in DNA microarray using artificial neural network

DNA microarrays are normally used to measure the expression values of thousands of several genes simultaneously in the form of large matrices. This raw gene expression data may contain some missing cells. These missing values may affect the analysis performed subsequently on these gene expression data. Several imputation methods, like K-Nearest Neighbor Imputation (KNNImpute), Singular Value Decomposition Imputation (SVDImpute), Local Least Square Imputation (LLSImpute), Bayesian Principal Component Analysis (BPCAImpute) etc. have already been proposed to impute those missing values. In this work we have proposed an ensemble classifier based Artificial Neural Network implementation, ANNImpute, to enhance the accuracy of the missing value imputation technique by applying Two Layer Perceptron Learning algorithm. Ensemble classification is done on the parameters such as learning rate a, weight vector & bias. We have applied our algorithm on two benchmark datasets like SPELLMAN and Tumour (GDS2932) and the results show that this approach performs well compared to the other existing methods as far as RMSE measures are concerned.

I. Introduction

A DNA microarray (also commonly known as DNA chip or biochip) contains a collection of microscopic DNA spots attached to a solid surface [1]. This gene expression data is used for applications like drug discovery, protein sequencing, cancer classification, and also for the identification of genes relevant to a certain diagnosis or therapy [1]. However due to various reasons, like corruption of the images, insufficient resolution etc., DNA microarray gene expression data may contain some missing cells [2]. A lot of information is lost when genes with missing values are ignored or directly deleted. Further data analysis on these data set with missing values may be seriously disturbed [2].