{
  "annotation_id": "d8891f56-027f-4e0e-b5ef-7245681d2398",
  "date_created": "2026-03-02T18:01:31.612000Z",
  "date_modified": "2026-03-02T18:01:31.612000Z",
  "file_hash": "653b225efe7004ff322a060751b29dfc5d380b24edccec3ae42e1ea3f46fee2f",
  "private": false,
  "record": {
    "abstract": "One of the major successes in computational biology has been the unification,\nusing the graphical model formalism, of a multitude of algorithms for\nannotating and comparing biological sequences. Graphical models that have been\napplied towards these problems include hidden Markov models for annotation,\ntree models for phylogenetics, and pair hidden Markov models for alignment. A\nsingle algorithm, the sum-product algorithm, solves many of the inference\nproblems associated with different statistical models. This paper introduces\nthe \\emph{polytope propagation algorithm} for computing the Newton polytope of\nan observation from a graphical model. This algorithm is a geometric version of\nthe sum-product algorithm and is used to analyze the parametric behavior of\nmaximum a posteriori inference calculations for graphical models.",
    "arxiv_id": "q-bio/0401033",
    "authors": [
      "Lior Pachter",
      "Bernd Sturmfels"
    ],
    "categories": [
      "q-bio.GN",
      "cs.LG",
      "math.ST",
      "stat.TH"
    ],
    "doi": "10.1073/pnas.0406011101",
    "title": "Parametric Inference for Biological Sequence Analysis",
    "url": "https://arxiv.org/abs/q-bio/0401033"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "886425f9-c1fd-4552-b407-7ca94bb617dc",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}