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Machine Learning and Network Science for Predicting Kidney Transplant Survival

Award Number: R01LM013311
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/01/2019
PERIOD OF PERFORMANCE END DATE: 12/31/2023

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Machine Learning and Network Science for Predicting Kidney Transplant Survival - Chronic kidney disease affects about 10% of adults in the United States and 7-12% of the population worldwide. It may lead to irreversible loss of kidney function, known as end-stage renal disease (ESRD). For patients with ESRD, kidney transplantation is the preferred treatment compared to dialysis in terms of patient survival, quality of life and cost. Despite the advantages of kidney transplants, most patients with ESRD are treated with dialysis primarily because there exist an insufficient number of compatible donors for patients. The human leukocyte antigens (HLAs) of the organ donor and recipient are known to be a significant contributing factor to transplanted organ survival times due to immunogenicity, the immune response of the recipient to the transplanted organ. Mismatches between donor and recipient HLAs are associated with shorter survival times; however, it is extremely rare to identify donors that have a perfect match with recipients, so most transplants involve mismatched HLAs. Our main objective is to accurately predict survival times for kidney transplants by incorporating both data- driven models of HLA compatibility based on outcomes of past transplants and biologically-driven models of HLA immunogenicity. Accurate prediction of survival times can improve patient transplant outcomes by enabling more efficient allocation of donors and recipients, particularly by reducing the number of repeat transplants due to graft failure with a poorly matched donor. We propose to estimate HLA compatibilities using high-dimensional variable selection techniques applied to outcomes of past transplants and through a novel donor-recipient latent space model for the HLA compatibility network. We then propose to incorporate these predicted compatibilities along with biologically-driven models of HLA immunogenicity using amino acid sequences and epitopes into a multi-task classification-based survival prediction algorithm. Our proposed approach for learning integrated data- and biologically-driven models of transplant survival generalizes broadly to organ transplantation (liver, heart, pancreas, lungs) and possibly to bone marrow transplantation.


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: 2024 ( Subtotal = -$2,045 )
2024	2021	UNIVERSITY OF TOLEDO	2801 W BANCROFT ST	TOLEDO	OH	43606	LUCAS	USA	Medical Library Assistance	000	3	6/28/2024	NON-COMPETING CONTINUATION	-$2,045
														Subtotal = -$2,045

Issue Date FY: 2023 ( Subtotal = $0 )
2023	2021	UNIVERSITY OF TOLEDO, THE	2801 W BANCROFT ST	TOLEDO	OH	43606	LUCAS	USA	Medical Library Assistance	000	3	1/6/2023	NON-COMPETING CONTINUATION	$0
2023	2021	UNIVERSITY OF TOLEDO, THE	2801 W BANCROFT ST	TOLEDO	OH	43606	LUCAS	USA	Medical Library Assistance	001	3	8/4/2023	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2021 ( Subtotal = $277,764 )
2021	2021	UNIVERSITY OF TOLEDO, THE	2801 W BANCROFT ST	TOLEDO	OH	43606	LUCAS	USA	Medical Library Assistance	000	3	7/19/2021	NON-COMPETING CONTINUATION	$277,764
														Subtotal = $277,764

Issue Date FY: 2020 ( Subtotal = $277,764 )
2020	2020	UNIVERSITY OF TOLEDO, THE	2801 W BANCROFT ST	TOLEDO	OH	43606	LUCAS	USA	Medical Library Assistance	000	2	7/21/2020	NON-COMPETING CONTINUATION	$277,764
														Subtotal = $277,764

Issue Date FY: 2019 ( Subtotal = $274,577 )
2019	2019	UNIVERSITY OF TOLEDO, THE	2801 W BANCROFT ST	TOLEDO	OH	43606	LUCAS	USA	Medical Library Assistance	001	1	7/31/2019	NEW	$153
2019	2019	UNIVERSITY OF TOLEDO, THE	2801 W BANCROFT ST	TOLEDO	OH	43606	LUCAS	USA	Medical Library Assistance	000	1	7/29/2019	NEW	$274,424
														Subtotal = $274,577

Grand Total All Awards = $828,060

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Machine Learning and Network Science for Predicting Kidney Transplant Survival

Award Number: R01LM013311

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 08/01/2019

PERIOD OF PERFORMANCE END DATE: 12/31/2023

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