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. Author manuscript; available in PMC: 2018 Sep 1.
Published in final edited form as: IEEE/ACM Trans Comput Biol Bioinform. 2016 Jan 26;14(5):1070–1081. doi: 10.1109/TCBB.2016.2520919

Fig. 5. FStitch requires little training data and is robust to low levels of GRO-seq read coverage.

Fig. 5

(A) Classification accuracy utilizing successively decreasing amounts of training data to learn feature vector weights, for the polynomial (d = 2 and c = 0; blue and teal) and linear (d = 1 and c = 0; green and red) kernel. (B) Classification accuracy with successively less sequencing depth (dataset size). In this case, we trained on 5% of all available chromosome 1 labels and tested on 50 different subsamples of the curated dataset. T P = true positive rate and F N = false negative rate.