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A machine-learning ...
A machine-learning method for biobank-scale genetic prediction of blood group antigens
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- Hyvärinen, Kati (författare)
- Finnish Red Cross Blood Transfusion Service
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- Haimila, Katri (författare)
- Finnish Red Cross Blood Transfusion Service
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- Moslemi, Camous (författare)
- Aarhus University Hospital,Zealand University Hospital
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- Biobank, Blood Service (författare)
- Finnish Red Cross Blood Transfusion Service
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- Olsson, Martin L. (författare)
- Lund University,Lunds universitet,Avdelningen för hematologi och transfusionsmedicin,Institutionen för laboratoriemedicin,Medicinska fakulteten,Transfusionsmedicin,Forskargrupper vid Lunds universitet,Division of Hematology and Transfusion Medicine,Department of Laboratory Medicine,Faculty of Medicine,Transfusion Medicine,Lund University Research Groups,Region Skåne
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- Ostrowski, Sisse R. (författare)
- Copenhagen University Hospital,University of Copenhagen
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- Pedersen, Ole B. (författare)
- University of Copenhagen,Zealand University Hospital
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- Erikstrup, Christian (författare)
- Aarhus University Hospital
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- Partanen, Jukka (författare)
- Finnish Red Cross Blood Transfusion Service
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- Ritari, Jarmo (författare)
- Finnish Red Cross Blood Transfusion Service
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: PLoS Computational Biology. - 1553-734X. ; 20:3
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9-100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1-99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1-99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank- scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Hematologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Hematology (hsv//eng)
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Hyvärinen, Kati
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Haimila, Katri
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Moslemi, Camous
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Biobank, Blood S ...
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Olsson, Martin L ...
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Ostrowski, Sisse ...
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visa fler...
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Pedersen, Ole B.
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Erikstrup, Chris ...
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Partanen, Jukka
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Ritari, Jarmo
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Hematologi
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PLoS Computation ...
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Lunds universitet