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Figure 1 | BMC Genomics

Figure 1

From: Assessment of computational methods for predicting the effects of missense mutations in human cancers

Figure 1

Overview of representative predictors. Predictors are annotated with the basis of their predictions, their cancer-specificity and reliance on each other. The pioneering SIFT method uses conservation information to predict the functional impact of amino acid changes. Several other approaches integrate SIFT results (arrows pointing to SIFT). The power of evolutionary information as an input feature is reflected by the number of classifiers that use conservation for prediction. For example, MAPP, SIFT, Align-GVGD, MutationAssessor and LogRE are predominantly based on conservation. PolyPhen-2 additionally integrates structure to classify mutations as deleterious or benign. Consensus classifiers such as Condel combine multiple predictive tools. The neural network-based SNAP represents one of several recently developed methods that rely on training sets and a large set of discriminatory features. Cancer-specific tools such as mCluster are specifically designed to identify driver mutations and also depend on mutation training sets. The machine learning based method CHASM spans an extensive feature space and is trained on canonical cancer driver mutations. In addition to evolutionary information, CanPredict takes into account gene ontology annotation for classifying oncogenes.

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