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• W2896089889 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." @default.
• W2896089889 created "2018-10-26" @default.
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• W2896089889 date "2018-10-16" @default.
• W2896089889 modified "2023-10-16" @default.
• W2896089889 title "Signed iterative random forests to identify enhancer-associated transcription factor binding" @default.
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• W2896089889 doi "https://doi.org/10.48550/arxiv.1810.07287" @default.
• W2896089889 hasPublicationYear "2018" @default.
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