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Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response

Award Number: R35GM133657
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response - PROJECT SUMMARY Alzheimer’s disease and its related dementias (ADRD) are a growing public health crisis with no known cure. Alzheimer’s disease (AD) is the most common cause of dementia, expected to affect about 50 million people globally by 2050. Despite extensive efforts, no effective treatment has been established. Recent efforts in drug development for AD have not advanced at the rate needed to discover new and diverse therapy modalities for the disease. Furthermore, due to complexity of AD pathogenesis and co- morbid conditions, combination drug therapy is essential for efficacious treatment for AD. As traditional drug discovery takes 10-15 years and costs $2-3 billion, computational drug discovery approaches have gained popularity in recent years. Particularly, repurposing existing approved drugs for new diseases could circumvent the labor- and cost-intensive process of traditional drug discovery. The aim of this project is to develop a novel machine learning method to integrate multi-modal drug, disease, and protein datasets to prioritize FDA-approved drugs and their combinations for AD/ADRD. To represent the multi- modal datasets and the multiple relationships between drugs, diseases, and targets, an attributed multiplex heterogeneous graph will be constructed. Inspired from growing research on deep graph representation learning methods, a novel deep learning architecture will be developed to learn node representations in this complex graph. Using these node representations, a machine learning model will be trained to rank drugs and their combinations based on their associations to AD and AD-related pathogenic processes and pathways. Using attention mechanisms, the proposed machine learning model will be interpretable. The expected outcome of this research is a ranked list of drug combinations for AD/ADRD, with novel drug-target interactions related to AD/ADRD along with key features and datasets that support these findings. The proposed project will complement the efforts of the parent award the goal of which is to develop open-source integrative computational tools that perform secondary analysis of publicly available multi-modal biological, clinical, and environmental exposure datasets to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient-specific drug response.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2023 ( Subtotal = $678,989 )
2023	2023	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	001	6	7/20/2023	NON-COMPETING CONTINUATION	$349,691
2023	2023	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	000	5	5/16/2023	SUPPLEMENT FOR EXPANSION	$8,825
2023	2023	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	002	6	8/2/2023	SUPPLEMENT FOR EXPANSION	$320,473
														Subtotal = $678,989

Issue Date FY: 2022 ( Subtotal = $461,066 )
2022	2022	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	000	5	7/11/2022	NON-COMPETING CONTINUATION	$349,691
2022	2022	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	001	5	9/19/2022	SUPPLEMENT FOR EXPANSION	$111,375
														Subtotal = $461,066

Issue Date FY: 2021 ( Subtotal = $314,703 )
2021	2021	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	002	4	7/7/2021	NON-COMPETING CONTINUATION	$342,307
2021	2020	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	005	3	7/11/2021	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$345,476
2021	2020	MARQUETTE UNIVERSITY	915 W WISCONSIN AVE RM 175	MILWAUKEE	WI	53233	MILWAUKEE	USA	Biomedical Research and Research Training	000	2	12/28/2020	NON-COMPETING CONTINUATION	-$177,201
2021	2020	MARQUETTE UNIVERSITY	915 W WISCONSIN AVE RM 175	MILWAUKEE	WI	53233	MILWAUKEE	USA	Biomedical Research and Research Training	004	2	7/11/2021	NON-COMPETING CONTINUATION	-$186,791
2021	2020	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Biomedical Research and Research Training	001	3	12/29/2020	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$149,597
2021	2019	MARQUETTE UNIVERSITY	915 W WISCONSIN AVE RM 175	MILWAUKEE	WI	53233	MILWAUKEE	USA	Biomedical Research and Research Training	003	1	7/11/2021	NEW	-$158,685
														Subtotal = $314,703

Issue Date FY: 2020 ( Subtotal = $363,992 )
2020	2020	MARQUETTE UNIVERSITY	915 W WISCONSIN AVE RM 175	MILWAUKEE	WI	53233	MILWAUKEE	USA	Biomedical Research and Research Training	000	2	6/10/2020	NON-COMPETING CONTINUATION	$363,992
														Subtotal = $363,992

Issue Date FY: 2019 ( Subtotal = $341,867 )
2019	2019	MARQUETTE UNIVERSITY	915 W WISCONSIN AVE RM 175	MILWAUKEE	WI	53233	MILWAUKEE	USA	Biomedical Research and Research Training	000	1	7/30/2019	NEW	$341,867
														Subtotal = $341,867

Grand Total All Awards = $2,160,617

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Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response

Award Number: R35GM133657

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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