
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | April 11, 2018 |
Latest Amendment Date: | April 11, 2018 |
Award Number: | 1827472 |
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: | February 1, 2018 |
End Date: | August 31, 2022 (Estimated) |
Total Intended Award Amount: | $898,355.00 |
Total Awarded Amount to Date: | $914,354.00 |
Funds Obligated to Date: |
FY 2017 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
3451 WALNUT ST STE 440A PHILADELPHIA PA US 19104-6205 (215)898-7293 |
Sponsor Congressional District: |
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Primary Place of Performance: |
423 Guardian Drive Philadelphia PA US 19104-4865 |
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): | Smart and Connected Health |
Primary Program Source: |
01001718DB 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
Recent advances in large-scale electronic health record database techniques provide exciting new opportunities to the study of drug safety. Drug-drug interactions (DDIs), a major cause of adverse drug events (ADEs), are a serious global health concern, and a severe detriment to public health. The scale of DDIs involving three or more drugs (also called high-order DDIs) has posed a prohibitory challenge for its molecular pharmacology and clinical research, which motivates alternative strategies such as mining health record data. This project aims to develop large-scale computational strategies and effective software tools for mining high-order DDI effects from health record databases, in order to yield novel discoveries in drug safety, and ultimately to benefit national health and well being.
To achieve the above goal, this project is designed to complete four specific tasks. Task 1 aims to develop a novel statistical framework to discover high-order DDI signals associated with ADEs from health record databases. Task 2 aims to study a novel drug safety problem for mining directional DDI signals. Task 3 aims to develop an innovative approach for mining directional DDI patterns at the drug-group level. Task 4 is devoted to software development, evaluation and validation. The project applies these methods to analyze three independent databases, packages method implementations into a user-friendly software toolkit, and releases the toolkit to the public. This project not only facilitates the development of novel computational techniques in drug safety research, but also addresses emerging scientific questions in modeling, mining, and visual exploration of complex data such as the health record data. The project's educational activities include course development, student mentoring and advising, and involvement of minority and underrepresented students in research activities.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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 statistical models for identifying high order drug-drug-interaction (DDI) effect on myopathy.
2. We proposed a novel pharmacovigilance problem for mining directional high order DDI effects on myopathy and developed multiple data mining and visualization methods for revealing directional DDI effects on myopathy.
3. We developed a hyper-graph-net approach for defining drug similarity. Based on this, we developed machine learning methods for detecting and predicting high-order DDI effects. We developed a method for mining DDIs at the drug group level, and a super-combo-drug test to detect high-order drug interactions and to handle strongly correlated drugs.
4. We created a web page for visual exploration of directional DDIs and released the code for high-order DDI prediction using deep learning.
5. We published over 20 full-length papers related to this project in peer-reviewed conference proceedings and journals.
6. This project supported the following students
a. University of Pennsylvania (Penn): Three female REU students in Computer and Information Science, one male REU student in Economics and Mathematical Biology, one male REU student in Bioengineering
b. Ohio State University (OSU): Seven graduate students.
c. Indiana University (before PI's transition to Penn): Two female PhD students in Bioinformatics, one PhD student in Computer Science, one PhD student in Genetics, one REU student and two high school students.
7. This project provided capstone project topics for the following two groups of students
a. Four senior students in Computer and information Science at Penn
b. Five senior students in Computer and Information Science at Penn
8. The research materials produced in this project are used in (1) teaching several graduate courses in Penn and OSU, and (2) giving numerous conference and seminar presentations.
9. We co-organized two conferences in related fields: International Conference on Intelligent Biology and Medicine (ICIBM 2020 and ICIBM 2021).
Last Modified: 12/29/2022
Modified by: Li Shen
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