Improving the Usability of Donor Data from Registries Using an Extreme Learning Machine Approach to Upgrade Low-/Mid- to High-Resolution HLA Data

Background/Objectives: High-resolution Human Leukocyte Antigen (HLA) typing is required for unrelated donor selection, yet many registry records remain low- or intermediate-resolution. Haplotype frequency imputation is the standard remedy, but its accuracy decreases for rare haplotypes.

Methods

A locus-specific Extreme Learning Machine (ELM) framework, trained on the Greek national donor registry (n = 117,345), upgrades low-/intermediate-resolution genotypes and imputes untyped loci; output weights are solved analytically. It was validated on two independent Greek cohorts (n = 20,100; 4353) against GRIMM and two expectation maximization baselines, on the same donors.