Validating text mining results on protein-protein interactions using gene expression profiles

Protein-protein interactions referring to the associations of protein molecules are crucial for many biological functions. Since most knowledge about them still hides in biological publications, there is an increasing focus on mining information from the vast amount of biological literature such as MedLine. Many approaches, such as pattern matching, shallow parsing and deep parsing, have been proposed to automatically extract protein-protein interaction information from text sources, with however limited success. Moreover, to the best of our knowledge, none of the existing approaches have performed automatic validation on the mining results. In this paper, we describe a novel framework in which text mining results are automatically validated using the knowledge mined from gene expression profiles. A probability model is proposed to score the confidence of protein-protein interactions based on both text mining results and gene expression profiles. Experimental results are presented to show the feasibility of this framework.