To this end, Boruta was used for the KLH7 data set, and the number of truly relevant variables was established. the linear models built on genes identified by a standard statistical analysis explain 1.5, 0.5, and 0.3% of variance for KLH, LTA, and LPS response, respectively. The present study shows that machine learning methods applied to systems with a complex interaction network can discover phenotype-genotype associations with much higher sensitivity than traditional statistical models. It adds contribution to evidence suggesting a role of MAPK8IP3 in the adaptive immune response. It also indicates that CRLF3 is involved in this?process as well. Both findings need additional verification. that is obtained using sensitivity analysis. Each tree in an ensemble is built using different samples of MLN1117 (Serabelisib) the data, and each split of the tree is built on a variable selected from a subset of all variables. The randomness injected in the process of tree construction has 2 effects. On one hand, it decreases the classification accuracy of an individual tree significantly, on the other, it decorrelates individual classifiers and helps to decrease overfitting. What is more, for each tree, there is a subset of objects not used for construction of this tree, the so-called out-of-bag (OOB) objects. This allows for unbiased estimate of the classification error and of trees that used is identified. Then for each tree from are randomly permuted among OOB objects, thus removing any information on the true values of parts. Then parts are used to generate the model, and the remaining part is used MLN1117 (Serabelisib) for evaluation. The procedure is repeated times, with each part serving once as a test set and times contributing to the training set. Such a procedure gives estimates both for average and SD of the models’ error. Unfortunately, when the sample size is small, the results may still depend on a particular split of data. In effect, both estimates may be biased by a particular split. To alleviate this problem, we repeat the cross-validation several times, with independent splits of data at each iteration. In the first step, the initial set of SNPs, that will be further optimised, is obtained. This step is performed in 99 independent repeats of the 3-fold cross-validation procedure (Figure?2). Within each iteration, first the set of relevant features is selected for MLN1117 (Serabelisib) the training set. Then the RF regression model is built using selected variables, and the quality MLN1117 (Serabelisib) of the model is tested on the test set. The feature selection is performed with the help CD133 of the resampling scheme, based on 10 repeats of 3-fold cross-validation. In each iteration, the training is split into 3 parts, and 3 different samples are created as a combination of these parts. Then the RF model is built on each sample, and the for each variable is collected. The sum of from the 30 samples is then used to rank variables. The 30 variables with the highest are selected. These variables are used to build a RF model for the training set and validate the prediction on the test set. The number of variables that are used for model building is a meta-parameter of the procedure, which was obtained with the help of MLN1117 (Serabelisib) Boruta algorithm for all-relevant feature selection. To this end, Boruta was used for the KLH7 data set, and the number of truly relevant variables was established. The number of relevant variables established for KLH7 was used as a parameter for all other data sets because we wanted to keep the number of hyperparameters in the protocol as low as possible. The initial models for KLH7 were best, and therefore, we decided to develop an entire protocol for this data set and then repeat without further optimisations for other data sets. The resampling scheme with.