![]() Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. They can also be used for almost any human cancer due to the broad tropism of the vectors. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10%-30% of the networks. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Identifying influencers from sampled social networks In conclusion, co-expression network analysis identified six hub genes in association with HCC metastasis risk and prognosis, which might improve the prognosis by influencing amino acid metabolism and oxidation. ![]() Gene set enrichment analysis (GSEA) demonstrated that in the samples with any hub gene highly expressed, a total of 24 functional gene sets were enriched, most of which focused on amino acid metabolism and oxidation. ![]() RNA-sequencing data of 142 HCC samples showed consistent results in the prognosis. No signals reached genome-wide significance (P 1). A genome-wide meta-analysis of blood pressure (BP) response to hydrochlorothiazide was performed in 1739 white hypertensives from 6 clinical trials within the International Consortium for Antihypertensive Pharmacogenomics Studies, making it the largest study to date of its kind. This study aimed to identify novel loci influencing the antihypertensive response to hydrochlorothiazide monotherapy. Salvi, Erika Wang, Zhiying Rizzi, Federica Gong, Yan McDonough, Caitrin W Padmanabhan, Sandosh Hiltunen, Timo P Lanzani, Chiara Zaninello, Roberta Chittani, Martina Bailey, Kent R Sarin, Antti-Pekka Barcella, Matteo Melander, Olle Chapman, Arlene B Manunta, Paolo Kontula, Kimmo K Glorioso, Nicola Cusi, Daniele Dominiczak, Anna F Johnson, Julie A Barlassina, Cristina Boerwinkle, Eric Cooper-DeHoff, Rhonda M Turner, Stephen T ![]() Genome-Wide and Gene-Based Meta-Analyses Identify Novel Loci Influencing Blood Pressure Response to Hydrochlorothiazide.
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