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Figure 5 | BMC Bioinformatics

Figure 5

From: Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Figure 5

Steps in generating and testing a weighted decision tree. To create a weighted decision tree, mutations from amyloidogenic and non-amyloidogenic derivatives of a single germline are organized into separate matrices that factor in location, exposure and sheet-forming propensity into account (Step 1). These matrices are visualized and analyzed for general trends that may be transformed into weights (Step 2). An initial tree is constructed from these information, which is tested against the training set (Step 3). From this testing, it became evident that certain paths can be used for maximally separating amyloidogenic and non-amyloidogenic derivatives of a germline, and that these paths are germline-dependent. We then generated a tree that takes the germline of origin into account, and which has different boosted paths. The final step was to generate the classification threshold, which was determined from the analysis of scores for the test set (Step 4). This tree was then used to classify sequences in an independent, holdout test set (Step 5).

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