Hierarchical clustering combining numerical and biological similarities for gene expression data classification

High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results. In this work, a knowledge integration scheme is proposed to improve the microarray classification results from [3]. Biological knowledge is used to infer biological similarity which is combined with the classical numerical similarity. The resulting similarity measure is used in a hierarchical clustering process producing new features called metagenes. The goal of the numerical and biological similarities integration is to produce metagenes involving more useful and significant gene signatures. The proposed algorithm has been tested on 7 publicly available datasets. The results have been compared with the state of the art method. The knowledge inclusion has proven beneficial both for the predictive ability, improving the results repeatability, and for the biological relevance after evaluating the produced signatures with two gene list analysis tools.