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  "Title": "preciseTAD: A machine learning framework for precise TAD\nboundary prediction",
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  "Description": "preciseTAD provides functions to predict the location of\nboundaries of topologically associated domains (TADs) and\nchromatin loops at base-level resolution. As an input, it takes\nBED-formatted genomic coordinates of domain boundaries detected\nfrom low-resolution Hi-C data, and coordinates of\nhigh-resolution genomic annotations from ENCODE or other\nconsortia. preciseTAD employs several feature engineering\nstrategies and resampling techniques to address class\nimbalance, and trains an optimized random forest model for\npredicting low-resolution domain boundaries. Translated on a\nbase-level, preciseTAD predicts the probability for each base\nto be a boundary. Density-based clustering and scalable\npartitioning techniques are used to detect precise boundary\nregions and summit points. Compared with low-resolution\nboundaries, preciseTAD boundaries are highly enriched for CTCF,\nRAD21, SMC3, and ZNF143 signal and more conserved across cell\nlines. The pre-trained model can accurately predict boundaries\nin another cell line using CTCF, RAD21, SMC3, and ZNF143\nannotation data for this cell line.",
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      "date": "2026-05-30T07:15:24.000Z",
      "commit": "9f07a14d91a01296708baf1bbb9854ec55c0872c",
      "fileid": "7e55bc0ad47070fe1a7f5f5c8fe0fae608083674aee613847109121b075068f2",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/bioc/actions/runs/26677584782"
    }
  ]
}