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Dennis Vitkup
Researcher at Columbia University
Publications - 68
Citations - 7524
Dennis Vitkup is an academic researcher from Columbia University. The author has contributed to research in topics: Metabolic network & Gene. The author has an hindex of 36, co-authored 65 publications receiving 6902 citations. Previous affiliations of Dennis Vitkup include Harvard University & Massachusetts Institute of Technology.
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Journal ArticleDOI
Genetic robustness and functional evolution of gene duplicates
Germán Plata,Dennis Vitkup +1 more
TL;DR: It is found that, owing to their high functional load, close duplicates are unlikely to provide substantial backup in the context of large natural populations and, Interestingly, as duplicates diverge from each other, their overall functional load is reduced.
Heterogeneity of tumor-induced gene expression changes in the human metabolic network
Jie Hu,Jason W. Locasale,Jason H. Bielas,Jason H. Bielas,Jacintha O'Sullivan,Kieran Sheahan,Lewis C. Cantley,Lewis C. Cantley,Matthew G. Vander Heiden,Matthew G. Vander Heiden,Dennis Vitkup +10 more
TL;DR: In this article, the expression patterns of metabolic genes across 22 diverse types of human tumors were compared and it was shown that the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues.
Journal ArticleDOI
The rate of the molecular clock and the cost of gratuitous protein synthesis
TL;DR: The results suggest that it is unlikely that selection against misfolding toxicity significantly affects the protein clock in species other than E. coli, and that in this bacterium other costs associated with protein synthesis are likely to play an important role.
Journal ArticleDOI
Why protein R-factors are so large: a self-consistent analysis.
TL;DR: The analysis shows that significant R‐factor values can arise from the use of isotropic B‐factors to model anisotropic protein motions and from coordinate errors.
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Automatic policing of biochemical annotations using genomic correlations
TL;DR: An automatic policing method to detect biochemical misannotations using context genomic correlations that works by finding genes with unusually weak genomic correlations in their assigned network positions is developed and optimized.