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The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Xiaoquan Wen is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 2021 |
Wen, Xiaoquan |
R35Activity Code Description:
To provide long term support to an experienced investigator with an outstanding record of research productivity. This support is intended to encourage investigators to embark on long-term projects of unusual potential. |
Resolving Methodological Challenges in Genomics Research: Causality, Risk Prediction, and Reproducibility @ University of Michigan At Ann Arbor
Project Summary With the availability of the high-through sequencing technology, the scientific community is now able to investigate complex phenotypes at both organismal and molecular levels. Nevertheless, it is still considerably difficult to perform controlled experiments and randomized trials to investigate the causal relationships between phenotypes at different levels. It is therefore critically important to perform causal inference based on the observational data. In this project, we will develop computational methods to facilitate systematic investigation of causal molecular mechanisms underlying complex disease process. Specifically, we will target three outstanding scientific issues: i) casual inference of molecular mechanisms of complex diseases; ii) analytic approaches for risk prediction utilizing genomic information and causal molecular mechanisms, and iii) statistical assessment of reproducibility in high-throughput genomic experiments. Finally, we will build user-friendly computational software packages and make them available to the broad community of biological and medical scientists.
|
0.964 |
| 2021 |
Wen, Xiaoquan |
R35Activity Code Description:
To provide long term support to an experienced investigator with an outstanding record of research productivity. This support is intended to encourage investigators to embark on long-term projects of unusual potential. |
Resolving Methodological Challenges in Genomics Research: Causality, Risk Prediction, and Reproducibility: Administrative Supplement @ University of Michigan At Ann Arbor
Project Summary This proposed project focuses on the statistical assessment of reproducibility in genomic and genetic applications. Reproducibility is a hallmark of scientific research. However, the quantitative tools for evaluating reproducibility and replicability are still lacking. We will develop and apply computational tools to assess reproducibility in findings from genetic and genomic research. We will build user-friendly computational software packages and make them available to the broad community of biological and medical scientists.
|
0.964 |