A scalable computational framework for predicting gene expression from candidate cis-regulatory elements

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Figure 4.
Figure 4.

ScPGE discovers different patterns by analyzing predictions. (A) A pattern found in true positives (TPs) that the regulatory effect of cCREs on target genes decreases with distance. (B) The cross-cell-type predictive performance of ScPGE, where K562→GM12878 indicates using a model trained on K562 data to predict GM12878 data. (C) The cross-species predictive performance of ScPGE, where K562→MEL indicates using a model trained on K562 data (Human) to predict MEL data (Mouse).

This Article

  1. Genome Res. 36: 361-374

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