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

Abstract

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.

References in corpus (4)

• Predictive learning via rule ensembles
• Iterative Random Forests to detect predictive and stable high-order interactions
• Interpreting Tree Ensembles with inTrees
• Interpreting Models via Single Tree Approximation

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