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مقاله پایان نامه ارشدم که درباره پیدا کردن ژن های سرطانیه بالاخره چاپ شد اینجا قابل مشاهده ست httpsdoiorg101016jcompbiomed2019103362 Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach Abstract Cancer driver genes CDGs are the genes whose mutations cause tumor growth Several computational methods have been previously developed for finding CDGs Most of these methods are sequencebased that is they rely on finding key mutations in genomic data to predict CDGs In the present work we propose iMaxDriver as a networkbased tool for predicting driver genes by application of influence maximization algorithm on human transcriptional regulatory network TRN In the first step of this approach the TRN is pruned and weighted by exploiting tumorspecific gene expression GE data Then influence maximization approach is used to find the influence of each gene The top genes with the highest influence rate are selected as the potential driver genes We compared the performance of our CDG prediction method with fifteen other computational tools based on a benchmark of three different cancer types Our results show that iMaxDriver outperforms most of the stateoftheart algorithms for CDG prediction Furthermore iMaxDriver is able to correctly predict many CDGs that are overlooked by all previously published tools Due to this relative orthogonality iMaxDriver can be considered as a complementary approach to the sequencebased CDG prediction methods