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Fig. 5 | Genome Biology

Fig. 5

From: Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Fig. 5

Applying Hi-C model on new CLL samples. a The auPRC values achieved by GM12878 and K562 Hi-C model, x-axis: new CLL samples. b, c The confusion matrices for 6 new CLL samples using K562 Hi-C model with threshold of 0.016 and GM12878 Hi-C model with threshold of 0.025. x-axis, true label; y-axis, predicted label; 0, negative; 1, positive. d Summary of the predicted chromatin interactions in the 6 new CLL samples and the differential chromatin interactions between uCLL and mCLL samples. e Conservation analysis of predicted chromatin interactions in new CLL samples. All pairs, all possible pairs used for prediction; y-axis, the proportion of total chromatin interactions that can be found in a particular number of samples. f Uniqueness analysis of open chromatin regions that overlap with Hi-C peaks from GM12878 cells in new CLL samples. All, all open chromatin regions; y-axis, the proportion of total chromatin interactions that can be found in a particular number of samples

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