Evaluation of similarity measures for gene expression data and their correspondent combined measures.
It is commonly considered that genes with similar expression profiles are functional related. And there are many ways to measure the similarity of gene expression data. Especially with the advent of lots of biological information, new combined measures have been constructed by combining the biological information with different similarity measures. However, it is not clear that what is the most suitable and effective measure for gene expression data and what is the most suitable measure to construct the most effective combined measure. In this paper, several similarity measures are analyzed and two new similarity measures are proposed. Their correspondent combined measures are also constructed by incorporating Gene Ontology annotations. All these measures are evaluated by their effectiveness in detecting functionally links in several different gene expression data by comparing with the protein-protein interaction database. The results show that the newly proposed measures and their correspondent combined measures are very effective and suitable for different datasets. And our methodology is applicable to evaluate new similarity measures and detect the best measure for a certain dataset.
Li
Gang-Guo
GG
Institute of Automation, National University of Defense Technology, Changsha, 410073, Hunan, China. ligangguo1982@126.com
Wang
Zheng-Zhi
ZZ
D030541:Databases, Genetic; D005069:Evaluation Studies as Topic; D015966:Gene Expression Regulation, Fungal; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism
D029701:Saccharomyces cerevisiae Proteins