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Standard ChIP-seq peak calling pipelines seek to differentiate biochemicallyreproducible signals of individual genomic elements from background noise.However, reproducibility alone does not imply functional regulation (e.g.,enhancer activation, alternative splicing). Here we present a general-purpose,interpretable machine learning method: signed iterative random forests (siRF),which we use to infer regulatory interactions among transcription factors andfunctional binding signatures surrounding enhancer elements in Drosophilamelanogaster.

Signed iterative random forests to identify enhancer-associated transcription factor binding