Signed iterative random forests to identify enhancer-associated transcription factor binding
Year: 2018
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Kumbier, Karl, Basu, Sumanta, Frise, Erwin, Celniker, Susan E., Brown, James B., Celniker, Susan, Yu, Bin
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2896089889