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Table 1 Summary of 33 computational algorithms included in this study

From: Comprehensive assessment of computational algorithms in predicting cancer driver mutations

ClassifierFeaturesMethodReference
CADDConservation, epigenetic signals, functional predictions, genetic context, and published predictorsLinear kernel support vector machineRentzsch et al. [6]
CanDrAStructural, evolutionary, and genomic features, published predictorsSupport vector machineMao et al. [7]
CHASMStructural, evolutionary, and genomic featuresRandom forestCarter et al. [8]
CTAT-cancerTransFIC, fathmm, chasm, candraPrincipal component analysis (PCA)Bailey et al. [9]
CTAT-populationSIFT, PolyPhen2, mutationAssessor, VESTPCABailey et al. [9]
DANNConservation, epigenetic signals, functional predictions, and genetic contextDeep neural networkQuang et al. [10]
DEOGEN2Evolutionary, protein, gene, pathway, PROVEANRandom forestRaimondi et al. [11]
EigenPrediction scores of other tools, allele frequencies, epigenomic signalsUnsupervised spectral approachIonita-Laza et al. [12]
Eigen-PCPrediction scores of other tools, allele frequencies, epigenomic signalsUnsupervised spectral approachIonita-Laza et al. [12]
FATHMM-diseaseSequence homologyHidden Markov modelsShihab et al. [13]
FATHMM-cancerSequence homologyHidden Markov modelsShihab et al. [14]
FATHMM-MKLConservation, epigenomic signalsMultiple kernel learningShihab et al. [15]
FATHMM-XFConservation, genomic features, epigenomic signalsMultiple kernel learningRogers [16]
GenoCanyonConservation, biochemical annotationPosterior probability by unsupervised statistical learningLu et al. [17]
Integrated_fitConsIntegrated epigenomic signalsINSIGHTGulko et al. [18]
LRTSequence homologyLikelihood ratio test of codon neutralityChun et al. [19]
M-CAPPublished predictors, conservationGradient boosting tree classifierJagadeesh et al. [20]
MetaLRNine prediction scores and allele frequencies in 1000GLogistic regressionDong et al. [21]
MetaSVMNine prediction scores and allele frequencies in 1000GRadial kernel support vector machineDong et al. [21]
MPCRegional missense constraint, missense badness, polyphen2Logistic regressionSamocha et al. [22]
MutationAssessorSequence homologyCombinatorial entropy formalismReva et al. [23]
MutationTaster2Conservation, genetic context, regulatory featuresNaïve Bayes classifierSchwarz et al. [24]
MutPredProtein structural and functional properties, conservation, SIFTRandom forestLi et al. [25]
MVPSequence and structural features, published predictors, conservationDeep neural networkQian et al. [26]
Polyphen2_HDIVEight sequence-based and three structure-based predictive featuresNaïve Bayes classifierAdzhubei et al. [27]
Polyphen2_HVAREight sequence-based and three structure-based predictive featuresNaïve Bayes classifierAdzhubei et al. [27]
PrimateAISequence homologyDeep residual neural networkSundaram et al. [28]
PROVEANSequence homologyDelta alignment scoreChoi et al. [29]
REVELPublished predictorsRandom forestIoannidis et al. [30]
SIFTSequence homology based on PSI-BLASTPosition-specific scoring matrixNg et al. [31]
SIFT4GSequence homology based on Smith-WatermannPosition-specific scoring matrixVaser et al. [32]
TransFICSIFT, Polyphen2, mutationAssessorTransformed functional impact scoresGonzalez-Perez [33]
VEST4Amino acid-related features, DNA context, conservation, protein structureRandom forestCarter et al. [34]