AI has emerged as a beacon of hope for individuals by analyzing a certain genetic variation in minimizing the risk of kidney transplantation. The evaluation of graft failure risks in kidney transplants has traditionally relied on HLA (Human Leukocyte Antigen) mismatches. A research team from the University of Pennsylvania has explored an innovative machine-learning algorithm that can help unveil the hidden connections between amino-acid mismatches (AA-MMs) and the likelihood of graft failure.
Their approach, termed FIBRES (Feature Inclusion Bin Evolver for Risk Stratification), utilizes evolutionary algorithms to automatically construct AA-MMs bins, minimizing the assumptions about bin composition. It helps in effectively stratifying the transplant pairs into high-risk and low-risk groups for graft survival. By analyzing a dataset of 1,66,754 dataset of kidney transplants of deceased donors from (the Scientific Registry of Transplant Recipients)SRTR data using the FIBRES approach, the researchers found the limitations of traditional methods in graft failure risk. They emphasized the role of amino acid variability, allowing FIBRES to identify more than twice the number of low-risk patients.
FIBRES harnessed an evolutionary algorithm to iteratively optimize the AA-MMs bins’ fitness for graft failure risk stratification. It selected higher performing bind as “parent “ for generating novel offspring bins by ‘recombining’ (i.e., crossover) and ‘mutating’ (i.e., replacing, adding, and deleting) the AA positions within bins. FIBRES incorporates a “risk strata minimum” to ensure the statistical reliability of the results obtained.
This approach is applied in three analyses:(1) constructing bins using AA-MMs across five HLA loci and comparing risk stratification, (2) Binning AA-MMs within each HLA separately, and (3) Evaluating the performance using cross-validation. It helped in enhancing the risk stratification compared to 0- ABDR antigen mismatch. It was found that 24.4% of kidney transplants were low risk by AA-MM assessment versus 9.1% by 0-ABDR. Cross-validation demonstrated the generalisability of FIBERS bin risk prediction, confirming their robustness.
The researchers highlighted that FIBRES could be more holistic in determining which AA-MMs impact risk. However, they require much larger datasets. In the future, the researchers aim to address limitations by (1) extending binning to additional HLA loci, (2) comparing results between first transplant and re-transplant recipients, and (3) adapting FIBERS to optimize bins that can stratify donor/recipient pairs into any number of risk groups, learn group cutoffs, and learn AA-MM weights to infer the importance of a given MM.
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Astha Kumari is a consulting intern at MarktechPost. She is currently pursuing Dual degree course in the department of chemical engineering from Indian Institute of Technology(IIT), Kharagpur. She is a machine learning and artificial intelligence enthusiast. She is keen in exploring their real life applications in various fields.
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