J Am Med Inform Assoc - Identifying disease genes and module biomarkers by differential interactions.

Tópicos

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Resumo

JECTIVE: A complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs.MATERIALS AND METHODS: In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods.RESULTS AND DISCUSSION: As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method.CONCLUSION: We proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.

Resumo Limpo

jectiv complex diseas general caus mutat multipl gene dysfunct multipl biolog process systemat identif causal diseas gene modul biomark can provid insight mechan under complex diseas help develop effici therapi effect drugsmateri method paper present novel approach predict diseas gene identifi dysfunct network modul base analysi differenti interact diseas control sampl contrast analysi differenti gene protein express wide adopt exist methodsresult discuss exampl appli method studi threestag microarray data gastric cancer identifi network modul modul biomark includ set gene relat gastric cancer impli predict power method result holdout valid data set show identifi modul can serv effect modul biomark accur detect diagnos gastric cancer therebi valid effici methodconclus propos new approach detect modul biomark diseas result gastric cancer demonstr differenti interact use detect dysfunct modul molecular interact network turn can use robust modul biomark

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