ArticleFrontiers in medicine2020
Hap-E Search 2.0: Improving the Performance of a Probabilistic Donor-Recipient Matching Algorithm Based on Haplotype Frequencies.
Article in Frontiers in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- The BIOPREVENT machine-learning algorithm predicts chronic graft-versus-host disease and mortality risk using posttransplant biomarkers.The Journal of clinical investigation · 2026Trial
- GRIMM-II: A two-stage real-time algorithm for nine-locus HLA imputation and matching with up to three mismatches.Human immunology · 2026Article
- GRIMM-II: A Two-Stage Real-Time Algorithm for Nine-Locus HLA Imputation and Matching with Up to Three Mismatches.bioRxiv : the preprint server for biology · 2026Article
- "30 years of global collaboration in transplantation and cellular therapy: WMDA's commitment to donor safety, ethical standards, and equitable access".Bone marrow transplantation · 2025Review
- Estimating HLA haplotype frequencies from homozygous individuals - A Technical Report.International journal of immunogenetics · 2021Article
- Estimation of German KIR Allele Group Haplotype Frequencies.Frontiers in immunology · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the setting of hematopoietic stem cell transplantation, donor-patient HLA matching is the prime donor selection criterion. Matching algorithms provide ordered lists of donors where the probability of a donor to be an HLA match is calculated in cases where either donor or patient HLA typing information is ambiguous or incomplete. While providing important information for the selection of suitable donors, these algorithms are computationally demanding and often need several minutes up to hours to generate search results. Here, we present a new search kernel implementation for Hap-E Search, the haplotype frequency-based matching algorithm of DKMS. The updated search kernel uses pre-calculated information on donor genotypes to speed up the search process. The new algorithm reliably provides search results in <1 min for a large donor database (>9 Mio donors) including matching and mismatching donors, even for frequent or incomplete patient HLA data where the matching list contains several thousand donors. In these cases, the search process is accelerated by factors of 10 and more compared to the old Hap-E Search implementation. The predicted matching probabilities of the new algorithm were validated with data from verification typing requests of 67,550 donor-patient pairs.
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