Figure 3

Results screenshots of EpiGRAPH's machine learning module predicting monoallelic gene expression. (a-c) These screenshots display the results of machine learning analyses comparing the promoter regions of monoallelically expressed genes (class = 1) with those of biallelically expressed genes (class = 0), each panel being based on different EpiGRAPH settings. The table values in the tables summarize the average performance of a linear support vector machine or alternative machine learning algorithms (c) that were trained and evaluated in ten repetitions of a tenfold cross-validation. Performance measures include mean correlation ('mean corr' column), prediction accuracy ('mean acc' column), sensitivity ('sens' column) and specificity ('spec' column). Additional columns display standard deviations observed among the repeated cross-validations with random partition assignment ('corr sd' and 'acc sd'), the number of variables in each attribute group ('#vars') and the total number of genomic regions included in the analysis ('#cases').