  Standard ChIP-seq peak calling pipelines seek to differentiate biochemically
reproducible 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 and
functional binding signatures surrounding enhancer elements in Drosophila
melanogaster.
