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Figure 4 | BMC Bioinformatics

Figure 4

From: A novel strategy for classifying the output from an in silicovaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

Figure 4

A graph of proteins from the combined training dataset using only two input variables to illustrate a rule-based approach for binary classification. Abbreviations: TMHMM_AA = number of amino acid residues in transmembrane helices (a transmembrane domain is expected to be greater than 18), WoLF PSORT = nearest neighbour score (16 = 50%). Triangles and circles indicate expected vaccine candidacy of proteins. The aim of the rule-based approach is to find the optimum threshold values that segregate majority of triangles from majority of circles. Best rule for binary classification is ‘NO if TMHMM_AA < 12 and WoLF PSORT < 15 (shaded area on graph) else YES’. Two examples of where YES and NO classification rules are broken are shown on graph. When this best rule was applied to the benchmark dataset the sensitivity and specificity were 0.43 and 0.97 respectively.

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