Construction of integrated knowledge map for understanding protein function in disease: cancer-driver mechanisms of beta catenin

Celen, Irem
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University of Delaware
Disease driver mechanisms of proteins are highly complex; therefore, there is a plethora of information available from extensive studies for these mechanisms. Nevertheless, handling big data is challenging and time-consuming. We developed a systems approach that consists of creating a knowledge map by acquiring a comprehensive information about multifunctional protein beta catenin via text-mining and data-mining tools, and analyzing the gathered information for understanding the roles of beta catenin in cancer development. Our approach enabled us to propose testable hypotheses regarding beta catenin's involvement in cancer development including: (i) novel roles of several beta catenin post-translational modification sites based on their mutation frequency in cancer cells overall and their co-occurrence patterns with other mutations in individual cancer types; and (ii) candidate beta catenin transcriptional targets and the processes that are involved in the cell. Our integrative systems approach, which allows analyzing the wealth of information that it captured, will be generally applicable to build knowledge maps that provide new insights into other genes and their involvement in disease thereby leading to drug-target relationships to address mechanisms for disease-inhibitors.