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Zheyang Wu

Researcher at Worcester Polytechnic Institute

Publications -  67
Citations -  976

Zheyang Wu is an academic researcher from Worcester Polytechnic Institute. The author has contributed to research in topics: Statistical power & Genetic association. The author has an hindex of 15, co-authored 63 publications receiving 787 citations. Previous affiliations of Zheyang Wu include University of New Orleans & Yale University.

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Exome-wide rare variant analysis identifies TUBA4A mutations associated with familial ALS.

Bradley N. Smith, +64 more
- 22 Oct 2014 - 
TL;DR: In this paper, an exome-wide rare variant burden analysis of 363 index cases with familial ALS (FALS) was performed and the results revealed an excess of patient variants within TUBA4A, the gene encoding the Tubulin, Alpha 4A protein.
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Interspecific Transfer of Bacterial Endosymbionts between Tsetse Fly Species: Infection Establishment and Effect on Host Fitness

TL;DR: The ability to transinfect tsetse flies is indicative of Sodalis ' recent evolutionary history with its t setse fly host and demonstrates that this procedure may be used as a means of streamlining future paratransgenesis experiments.
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Statistical power of model selection strategies for genome-wide association studies

TL;DR: A novel statistical approach for power calculation is developed, accurate formulas for the power of different model selection strategies are derived, and the formulas are utilized to evaluate and compare these strategies in genetic model spaces.
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Detection boundary and Higher Criticism approach for rare and weak genetic effects

TL;DR: A new theoretical framework in large-scale inference to assess the joint significance of such rare and weak effects for a quantitative trait is developed, and it is shown that the HC-type methods are optimal in that they reliably yield detection once the parameters of the genetic effects fall above the detection boundary.
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HGF/c-Met axis drives cancer aggressiveness in the neo-adjuvant setting of ovarian cancer

TL;DR: Mir-193a-5p, HGF and c-Met expression may help select eligible patients for this modality of treatment, and inhibitors of this pathway may improve the efficacy of NACT.