Signed iterative random forests to identify enhancer-associated transcription factor binding
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.
Code (1)
Tasks
Interpretable Machine LearningSimilar Papers 제목 키워드 기반
Iterative Random Forests to detect predictive and stable high-order interactions
Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements tha…
Vocal Bursts Intensity PredictionHopular: Modern Hopfield Networks for Tabular Data
While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Support Vector Machines (SVMs), Random Forests…
Deep LearningGeneral ClassificationExogenous Randomness Empowering Random Forests
We offer theoretical and empirical insights into the impact of exogenous randomness on the effectiveness of random forests with tree-building rules independent of training data. We formally introduce the concept of exoge…
Unified Face Analysis by Iterative Multi-Output Random Forests
In this paper, we present a unified method for joint face image analysis, i.e., simultaneously estimating head pose, facial expression and landmark positions in real-world face images. To achieve this goal, we propose a …
Nonparametric Feature Selection by Random Forests and Deep Neural Networks
Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and useless features. Herein, we propose a nonpar…
BIG-bench Machine LearningComputational Efficiencyfeature selection