| Application: | 22783, The joint analysis of imaging data and genetic data for early tumor detection, prevention, diagnosis and treatment |
| Title: | A Powerful Global Test Statistic for Functional Statistical Inference |
| Size: | 1.5 MB |
| Archived: | 17 May 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3415
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Notes
We consider the problem of performing an association test between functional data and scalar variables in a varying coefficient model setting. We propose a functional projection regression model and an associated global test statistic to aggregate relatively weak signals across the domain of functional data, while reducing the dimension. An optimal functional projection direction is selected to maximize signal-to-noise ratio with ridge penalty. Theoretically, we systematically study the asymptotic distribution of the global test statistic and provide a strategy to adaptively select the optimal tuning parameter. We use simulations to show that the proposed test outperforms all existing state-of-the-art methods in functional statistical inference. Finally, we apply the proposed testing method to the genome-wide association analysis of imaging genetic data in UK Biobank dataset.Application 22783
The joint analysis of imaging data and genetic data for early tumor detection, prevention, diagnosis and treatment
We intend to explore different imaging and genetic characteristics to work on automatic early tumor detection, benign and malignant tumor discrimination, and prediction of tumor progression. These are critical in human cancer research because in our understanding, existing automatic cancer detection approaches using imaging modalities are rare, while by only recognizing possible physical warning signs of cancer for early diagnosis is not accurate enough. Furthermore, investigation into genetic factor and other risk factors including body mass index, medications, smoking and dietary factors, etc., will hopefully lead to the prevention and improvement of the cancer tumor symptoms. Our methods directly addresses the aim of UK Biobank for tumor detection, prevention, diagnosis and treatment. We will make use of the wealth of UK Biobank data to provide important new insights into the cancer research. And it will lead to an improved understanding of genetic mechanism in tumor progression and tumor imaging characteristics which has the potential to inspire new and urgently needed approaches to prevention, diagnosis, and treatment of cancer. Initially we implement machine learning techniques to automatically classify the tumor area for all imaging modalities. Imaging characteristics are derived after preprocessment of the raw imaging data to discriminate different tumor types. Second, we use different statistical approaches to select oncogenes and tumor suppressor genes. A genetic-imaging interactive analysis will be conducted for predictions of tumor progression and treatment. We will develop different novel statistical tools, find those to handle the dataset more efficiently, verify efficacy of the newly developed statistical tools using simulations and existing studies, and finally get the clinical conclusions and develop companion software. The full cohort data if applicable.
| Lead investigator: | Professor Hongtu Zhu |
| Lead institution: | University of North Carolina at Chapel Hill |
3 related Returns
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 3421 | 22783 | Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits | 18 May 2021 |
| 3419 | 22783 | Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies | 18 May 2021 |
| 3420 | 22783 | Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706) | 18 May 2021 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3416 | A Powerful Global Test Statistic for Functional Statistical Inference. | Jingwen Zhang (+3) | 2019 | Proceedings of the AAAI Conference on Artificial Intelligence |
Enabling scientific discoveries that improve human health