Additional file 2: Figure S2. of Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence Elena Campa Natàlia Padilla Xavier Cruz 10.6084/m9.figshare.c.3851767_D10.v1 https://springernature.figshare.com/articles/figure/Additional_file_2_Figure_S2_of_Development_of_pathogenicity_predictors_specific_for_variants_that_do_not_comply_with_clinical_guidelines_for_the_use_of_computational_evidence/5305813 Obtention of specific predictors for PRDIS variants. For each combination of the five reference methods used in this work (SIFT, PolyPhen-2, PON-P2, CADD and MutationTaster2) we obtained PRDIS, the subset of those variants for which the reference predictors disagreed. Then, for each of these PRDIS sets, we produced four different predictors, which differed either in the neural network model or in the neural network input. For the neural network model we tried two options: (i) no hidden layers (NN: 0); and (ii) one hidden with two nodes (NN: 2). For the neural network inputs, we tried two options: (i) the scores of the reference predictors; and (ii) the scores of the reference predictors enriched with three biological features (Blosum62 matrix elements, Shannon’s entropy, Position-specific scoring matrix elements; see Materials and Methods). Boxed in red is the case where PRDIS was obtained using SIFT and PolyPhen-2 as reference methods. (PNG 666 kb) 2017-08-11 05:00:00 In silico pathogenicity predictors Protein sequence variants Molecular diagnostics Missense variants Next-generation sequencing