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

Autor:	Kumbier, Karl, Basu, Sumanta, Frise, Erwin, Celniker, Susan E., Brown, James B., Celniker, Susan, Yu, Bin
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.07287 Zobrazit plný text záznamu View this record from Arxiv