Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes

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Figure 6.
Figure 6.

Analysis of kmer-SVM classifier trained with putative melanocyte enhancers. (A) Receiver operating characteristic curve for kmer-SVM classifier trained on putative melanocyte enhancers, with overall area under curve (auROC = 0.912) (B) Precision-recall curve with area under curve (auPRC = 0.297). (C) 6-mers with highest positive predictive value to kmer-SVM classifier, and factors predicted to bind each 6-mer. (*) No PWM in queried databases. Match based on similarity to published binding specificities (see Methods).

This Article

  1. Genome Res. 22: 2290-2301

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