# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "preciseTADhub" in publications use:'
type: software
license: MIT
title: 'preciseTADhub: Pre-trained random forest models obtained using preciseTAD'
version: 1.20.0
doi: 10.32614/CRAN.package.preciseTADhub
abstract: 'An experimentdata package to supplement the preciseTAD package containing
  pre-trained models and the variable importances of each genomic annotation used
  to build the model parsed into list objects and available in ExperimentHub. In total,
  preciseTADhub provides access to n=84 random forest classification models optimized
  to predict TAD/chromatin loop boundary regions and stored as .RDS files. The value,
  n, comes from the fact that we considered l=2 cell lines GM12878, K562, g=2 ground
  truth boundaries Arrowhead, Peakachu, and c=21 autosomal chromosomes CHR1, CHR2,
  ..., CHR22 (omitting CHR9). Furthermore, each object is itself a two-item list containing:
  (1) the model object, and (2) the variable importances for CTCF, RAD21, SMC3, and
  ZNF143 used to predict boundary regions. Each model is trained via a "holdout" strategy,
  in which data from chromosomes CHR1, CHR2, ..., CHRi-1, CHRi+1, ..., CHR22 were
  used to build the model and the ith chromosome was reserved for testing. See https://doi.org/10.1101/2020.09.03.282186
  for more detail on the model building strategy.'
authors:
- family-names: Stilianoudakis
  given-names: Spiro
  email: stilianoudasc@vcu.edu
- family-names: Dozmorov
  given-names: Mikhail
  email: mikhail.dozmorov@gmail.com
repository: https://bioconductor.org/
repository-code: https://github.com/dozmorovlab/preciseTADhub
commit: 591f5abbecd02cb98b8a0978c27d66b35d58ff26
url: https://github.com/dozmorovlab/preciseTADhub
date-released: '2026-04-28'
contact:
- family-names: Dozmorov
  given-names: Mikhail
  email: mikhail.dozmorov@gmail.com