Predicting incomplete gene microarray data with the use of supervised learning algorithms

Abstract

With the wealth of sequence data and the huge amount of data generated from molecular technologies, the issue of gene classification/prediction has become a central challenge in the field of microarray data analysis. This has led to the application of many well-established supervised learning (SL) algorithms in an attempt to provide more accurate and automatic diagnosis class (cancer/non cancer) prediction. Virtually all research on SL addresses the task of learning to classify complete domain instances. However, in some research situations we often have to classify instances given incomplete vectors, which can affect the predictive accuracy of learned classifiers. The task of learning an accurate incomplete data classifier from instances raises a number of new issues some of which have not been properly addressed by bioinformatics research. Thus, an effective missing value estimation method is required for improving predictive accuracy. Results: The essence of the approach is the proposal that prediction using supervised learning can be improved in probabilistic terms given incomplete microarray data. This imputation approach is based on the a priori probability of each value determined from the instances at that node of a decision tree (PDT) that have specified values. The proposed approach exploits the total probability and Bayes’ theorems and it has three versions. We evaluate our approach with other supervised learning techniques including C5.0, classification and regression trees (CART), k-nearest neighbour (k-NN), linear discrimination (LD) naïve Bayes classifier (NBC), Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and support vector machines (SVMs), from the point of view of their effect or tolerance of incomplete test data. Eight cancer related gene expression datasets are utilized for this task. Experimental results are provided to illustrate the efficiency and the robustness of the proposed algorithm.