James Lowey
James Lowey

James Lowey

Chief Information Officer


James Lowey

Mr. Lowey is TGen's Chief Information Officer. Mr. Lowey Provides the strategic vision and leadership for all aspects related to Information Technology at TGen. This includes working closely with scientific and administrative leadership to ensure the right technology is in place at the right time.

Some of this technology includes the TGen HPC environment; a dedicated scientific resource including a three thousand-plus core cluster supercomputer, various SMP and GPU based computational resources, and several petabytes of storage.

Mr. Lowey is also responsible for the security and availability of networks, servers and cloud-based services for a variety of scientific and administrative applications as well as data repositories and transfer mechanisms for both TGen and its collaborators.

Mr. Lowey joined TGen in 2003 to build large-scale HPC systems to aid scientists bringing bio-medical research to Arizona. He works closely in conjunction with researchers in the application of computing to life-sciences. To help facilitate this, he and his team have brought two supercomputer systems to TGen that have placed in the top 100 most powerful supercomputers in the world (www.top500.org) and within the top 10 supercomputers that are dedicated to life sciences research. He works closely with TGen scientists to implement and provide computational tools and data management systems to help facilitate and accelerate translational genomics research and to help expedite patient treatments.

Prior to joining TGen, Mr. Lowey worked as a consultant for various Fortune 500 companies, designing, building and managing large-scale computational systems to solve complex problems and process large amounts of data in a timely fashion.

The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk. Xu J, Lowey J, Wiklund F, Sun J, Lindmark, F, Hsu F-C, Dimitrov L, Chang B, Turner AR, Liu W, Adami H-O, Suh E, Moore JH, Zheng SL, Isaacs WB, Trent JM, Grönberg H. Cancer Epidemiol Biomarkers Prev 2005;14:2563-2568.

Optimal number of features as a function of sample size for various classification rules. Hua J, Xiong Z, Lowey J, Suh E, Dougherty ER. Bioinformatics 2005 21(8):1509-1515.

Impact of error estimation on feature selection. Simaa C, Attoor S, Brag-Netob U, Lowey J, Suh E, Dougherty ER. Pattern Recognition, 2005:38;2472-2482.

Genetic test bed for feature selection. Ashish Choudhary, Marcel Brun, Jianping Hua, James Lowey, Edward Suh, Edward R. Dougherty. Bioinformatics 22(7): 837-842 (2006).

Noise-injected neural networks show promise for use on small-sample expression data. Hua J, Lowey J, Xiong Z, Dougherty ER. BMC Bioinformatics. 2006 May 31;7:274.

Normalization benefits microarray-based classification. Hua J, Balagurunathan Y, Chen Y, Lowey J, Bittner ML, Xiong Z, Suh E, Dougherty ER. EURASIP J Bioinform Syst Biol. 2006:43056.

Statistical comparison framework and visualization scheme for ranking-based algorithms in high-throughput genome-wide studies. Tembe WD, Pearson JV, Homer N, Lowey J, Suh E, Craig DW. J Comput Biol. 2009 Apr;16(4):565-77.

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