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

The reliability of unrelated-donor searches depends on high-resolution HLA typing, yet a large fraction of records in national stem-cell donor registries were generated at low or intermediate resolution and are therefore under-used in modern matching. Here we develop an Extreme Learning Machine (ELM) approach that upgrades low/mid- to high-resolution HLA data by learning the haplotype and diplotype structure of a national donor population and assigning the most probable high-resolution genotypes together with posterior probabilities. The model was trained on the Greek national registry (Hellenic Transplant Organization, established 2002; 117,345 donors, ~20% low-resolution) and validated on two independent Greek cohorts (ORAM, n = 20,100; GRPT, n = 4,353) using accuracy and call-rate metrics. The population-specific ELM achieved a per-locus accuracy of 70–94% (depending on the confidence threshold) with an overall call rate of 98.1%, recovering usable high-resolution information and increasing the proportion of registry donors usable in high-resolution matching. The method is fast, lightweight and population-tailored, complementing established expectation-maximisation imputation tools.