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arXiv · 1810.07287

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

Karl Kumbier·Sumanta Basu·Erwin Frise·Susan E. Celniker·James B. Brown·Susan Celniker·Bin Yu
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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.

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Karl Kumbier, Sumanta Basu, Erwin Frise, Susan E. Celniker, James B. Brown, Susan Celniker, Bin Yu. 2018-10-16. Signed iterative random forests to identify enhancer-associated transcription factor binding. https://arxiv.org/abs/1810.07287

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