{
  "annotation_id": "4c7a2b58-33e4-4575-a0bc-7d61bfdd55a9",
  "date_created": "2026-03-02T18:01:31.667000Z",
  "date_modified": "2026-03-02T18:01:31.667000Z",
  "file_hash": "0557979fda89f1c20aeb0fab3b15ce1b5ecf6291dfe2fc6cd2e741cb7c2d9ab1",
  "private": false,
  "record": {
    "abstract": "We perform a reverse engineering from the ``extended Spellman data\u0027\u0027,\nconsisting of 6178 mRNA levels measured by microarrays at 73 instances in four\ntime series during the cell cycle of the yeast Saccharomyces cerevisae. By\nassuming a linear model of the genetic regulatory network, and imposing an\nextra constraint (the Lasso), we obtain a unique inference of coupling\nparameters. These parameters are transfered into an adjacent matrix, which is\nanalyzed with respect to topological properties and biological relevance. We\nfind a very broad distribution of outdegrees in the network, compatible with\nearlier findings for biological systems and totally incompatible with a random\ngraph, and also indications of modules in the network. Finally, we show there\nis an excess of genes coding for transcription factors among the genes of\nhighest outdegrees, a fact which indicates that our approach has biological\nrelevance.",
    "arxiv_id": "q-bio/0403012",
    "authors": [
      "Mika Gustafsson",
      "Michael Hornquist",
      "Anna Lombardi"
    ],
    "categories": [
      "q-bio.MN"
    ],
    "title": "Large-scale reverse engineering by the Lasso",
    "url": "https://arxiv.org/abs/q-bio/0403012"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0d8f96b8-43eb-42b4-aa03-3cf457b23722",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}