Fig 1.
Random forest classification models were generated using Finnish reference data set (n = 1,192). Allele dosages of genes determining RBC antigens/phenotypes and HPA-1 were combined with the antigen typing data. The dataset was divided randomly to train and test data sets. Random forest modelling was executed in the training data set (n = 596) and the important variables were selected using permutation. The models were evaluated in the test data set (n = 596) for prediction accuracy and errors. The final models were fitted using the full data set and both models and the method were validated in the Danish cohort (n = 111,677).
Table 1.
Blood group/HPA-1 antigen typing information of the Finnish and Danish cohorts.
Fig 2.
Summary of prediction accuracy metrics.
Distributions of accuracy metrics over all antigens shared by different test data sets. (A) Finnish random forest models evaluated in the Finnish test data set. (B) Finnish gradient boosting models evaluated in the Finnish test data set. (C) Finnish random forest models evaluated in the Danish full data set. (D) Danish random forest models evaluated in the Danish test data set. NPV, negative predictive value; PPV, positive predictive value.
Table 2.
Characteristics of the Finnish classification models.
Table 3.
Balanced accuracies for the Finnish and Danish models in full data sets.
Fig 3.
Input data for model fitting include target genotype and phenotype data and gene-phenotype data provided in the GitHub repository https://github.com/FRCBS/Blood_group_prediction. Outputs of the classification are models for the target antigens and related accuracy information.
Fig 4.
Application of the Finnish models.
Input data for application include target genotype data and Finnish model file provided in the GitHub repository https://github.com/FRCBS/Blood_group_prediction. Outputs of the classification are prediction results for the antigens.