
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | July 6, 2011 |
Latest Amendment Date: | April 5, 2012 |
Award Number: | 1117965 |
Award Instrument: | Standard Grant |
Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | August 1, 2011 |
End Date: | July 31, 2016 (Estimated) |
Total Intended Award Amount: | $299,904.00 |
Total Awarded Amount to Date: | $315,904.00 |
Funds Obligated to Date: |
FY 2012 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
701 S NEDDERMAN DR ARLINGTON TX US 76019-9800 (817)272-2105 |
Sponsor Congressional District: |
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Primary Place of Performance: |
701 S NEDDERMAN DR ARLINGTON TX US 76019-9800 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Info Integration & Informatics |
Primary Program Source: |
01001213DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Today's massive generation of digital data is greatly outpacing the development of computational methods and tools and presents critical challenges for achieving the full transformative potential of these data. For example, recent advances in acquiring multi-modal brain imaging and genome-wide array data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Major computational challenges are, however, bottlenecks for comprehensive joint analysis of these data due to their unprecedented scale and complexity. This project will employ the new capabilities of large-scale data mining techniques in multi-view learning, multi-task learning, and robust classification to address critical challenges in systematically analyzing massive multi-modal genetic, imaging, and other biomarker data. Specifically, this project will: (1) develop new multi-view learning methods to detect task-relevant phenotypic biomarkers from large scale heterogeneous imaging and other biomarker data, (2) implement new sparse multi-task regression models to reveal the genetic basis of phenotypic biomarkers at multiple levels (e.g., SNP, haplotype, gene and/or pathway), (3) design novel robust classification methods via structural sparsity for outcome prediction using integrated genotypic and phenotypic data, and (4) package these new methods into a data mining toolkit and release it to the public.
The intellectual merits of this project derive not only from the development of novel data mining methods, but also from their application to imaging genetic studies. These methods are designed to take into account interrelated structures among multiple data modalities and offer systematic strategies to reveal structural imaging genetic associations. The proposed methods and tools are expected to impact neurological and psychological research and enable investigators to effectively test imaging genetics hypothesis and advance biomedical science and technology. In addition, the proposed data mining framework addresses generic critical needs of large-scale data analysis and integration and, therefore, will impact a large number of research areas where high-value knowledge and complex patterns can potentially be discovered from massive high-dimensional and heterogeneous data sets. This project will facilitate the development of novel educational tools to enhance several current courses at UT Arlington and IUPUI. Both universities are minority-serving institutions, and the PIs will engage the minority students and under-served populations in research activities to give them a better exposure to cutting-edge scientific research.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The investigation of this project produces several important outcomes.
1. We developed multiple sparse multi-view learning algorithms for identifying imaging and fluid biomarkers related to cognitive and diagnostic outcomes.
2. We developed several sparse multi-task regression and correlation algorithms for identifying genetic variants related to imaging and other phenotypic outcomes.
3. We developed a sparse multimodal multitask learning algorithm for outcome prediction via integrating imaging and genetics data.
4. We released a sparse learning software tool.
We published over 20 full-length papers related to this project in peer-reviewed conference proceedings and journals.
This project supported three Ph.D. students (one of them is female) at University of Texas at Arlington. Two of them have graduated and one of them becomes a tenure-track assistant professor in Colorado School of Mines. The third one (female) is currently a fourth year Ph.D. student in the Computer Science and Engineering department, and will graduate next year with looking for an academic position.
This project also supported two male undergraduate REU students.
The research materials produced in this project are used in teaching several graduate courses at University of Texas at Arlington.
We (both UTA and IU sites) co-organized several workshops and one special session in related fields: (1) two MICCAI Workshops on Multimodal Brain Image Analysis (MBIA 2012 and MBIA 2013), (2) one MICCAI Workshop on Imaging Genetics (MICGen 2015), and (3) one Special Session on Neuroimaging Data Analysis and Applications at International Conference on Brain Informatics & Health (BIH 2015).
Last Modified: 11/30/2016
Modified by: Heng Huang
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