{
  "annotation_id": "87c63c23-d8f4-45ac-8a03-580811c33f92",
  "date_created": "2026-03-02T18:01:32.173000Z",
  "date_modified": "2026-03-02T18:01:32.173000Z",
  "file_hash": "81e49961843bf1c9c24edf3c453667ea8f7e39d707668e6e2632880504b65839",
  "private": false,
  "record": {
    "abstract": "This paper deals with gene networks whose dynamics is assumed to be generated\nby a continuous-time, linear, time invariant, finite dimensional system (LTI)\nat steady state. In particular, we deal with the problem of network\nreconstruction in the typical practical situation in which the number of\navailable data is largely insufficient to uniquely determine the network. In\norder to try to remove this ambiguity, we will exploit the biologically a\npriori assumption of network sparseness, and propose a new algorithm for\nnetwork reconstruction having a very low computational complexity (linear in\nthe number of genes) so to be able to deal also with very large networks (say,\nthousands of genes). Its performances are also tested both on artificial data\n(generated with linear models) and on real data obtained by Gardner et al. from\nthe SOS pathway in Escherichia coli.",
    "arxiv_id": "q-bio/0401044",
    "authors": [
      "Lorenzo Farina",
      "Ilaria Mogno"
    ],
    "categories": [
      "q-bio.QM",
      "q-bio.GN"
    ],
    "title": "A Fast Reconstruction Algorithm for Gene Networks",
    "url": "https://arxiv.org/abs/q-bio/0401044"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d134c9bd-9232-4b4e-ab91-b40d9fb6adc8",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}