Imputing missing values in microarray data with ontology information

Microarray technology is a big step in bioinformatics. Hidden information within the large amounts of data provides scientists with molecular functions or essential biological meanings to study and analyze. However, these data often contain a certain portion of entities that are missing. Several methods to estimate these missing values are developed, but most of them are with disadvantages. In this paper, we propose a novel approach to deal with these missing values based on a practical similarity measurement between gene pairs. Our approach takes gene expression values and gene ontology (GO) information for genes into consideration. We implement our approach on a real microarray dataset and compare its imputation accuracy with other methods. Experimental results show that our approach can estimate missing values in microarray data effectively.