# Machine Learning and Network Science for Predicting Kidney Transplant Survival

> **NIH NIH R01** · UNIVERSITY OF TOLEDO · 2020 · $277,764

## Abstract

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.

## Key facts

- **NIH application ID:** 9985180
- **Project number:** 5R01LM013311-02
- **Recipient organization:** UNIVERSITY OF TOLEDO
- **Principal Investigator:** Tian Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $277,764
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9985180

## Citation

> US National Institutes of Health, RePORTER application 9985180, Machine Learning and Network Science for Predicting Kidney Transplant Survival (5R01LM013311-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9985180. Licensed CC0.

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