{
  "annotation_id": "3824ff06-e8d0-4fe9-a277-f12cf8ea1d74",
  "date_created": "2026-03-02T18:01:31.672000Z",
  "date_modified": "2026-03-02T18:01:31.672000Z",
  "file_hash": "2b5d4cafdf5ebcf4d22c2bce4b1f607ff45227f8ac90b22bb24c65361ceb0faa",
  "private": false,
  "record": {
    "abstract": "We define the concept of dependence among multiple variables using maximum\nentropy techniques and introduce a graphical notation to denote the\ndependencies. Direct inference of information theoretic quantities from data\nuncovers dependencies even in undersampled regimes when the joint probability\ndistribution cannot be reliably estimated. The method is tested on synthetic\ndata. We anticipate it to be useful for inference of genetic circuits and other\nbiological signaling networks.",
    "arxiv_id": "q-bio/0406015",
    "authors": [
      "Ilya Nemenman"
    ],
    "categories": [
      "q-bio.QM",
      "cs.IT",
      "math.IT",
      "math.ST",
      "physics.data-an",
      "q-bio.GN",
      "stat.TH"
    ],
    "title": "Information theory, multivariate dependence, and genetic network   inference",
    "url": "https://arxiv.org/abs/q-bio/0406015"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "44c91b87-49ef-43f7-9fda-06afd48d7f10",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}