Reply to ‘Inflated performance measures in enhancer–promoter interaction-prediction methods’

Inflated performance measures in enhancer–promoter interaction-prediction methods

(2019) Fan Cao et al.   NATURE GENETICS

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

(2018) David R. Kelley et al.   GENOME RESEARCH

Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy

(2018) Wang Xi et al.   PLoS Computational Biology

Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines

(2017) Qin Cao et al.   NATURE GENETICS

The haploinsufficient tumor suppressor, CUX1, acts as an analog transcriptional regulator that controls target genes through distal enhancers that loop to target promoters

(2017) Robert K. Arthur et al.   NUCLEIC ACIDS RESEARCH

cis -Acting Complex-Trait-Associated lincRNA Expression Correlates with Modulation of Chromosomal Architecture

(2017) Jennifer Yihong Tan et al.   Cell Reports

Ten quick tips for machine learning in computational biology

(2017) Davide Chicco   BioData Mining

Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin

(2016) Sean Whalen et al.   NATURE GENETICS

High-throughput functional testing of ENCODE segmentation predictions

(2014) Jamie C. Kwasnieski et al.   GENOME RESEARCH