{
  "annotation_id": "e57c35e3-405c-40e9-983c-be97110d7a8c",
  "date_created": "2026-03-02T18:01:32.284000Z",
  "date_modified": "2026-03-02T18:01:32.284000Z",
  "file_hash": "1075c0e457101d17650d83781f581dc21209ff23abf47a355c601738abfe5dfc",
  "private": false,
  "record": {
    "abstract": "Support vector machines and kernel methods are increasingly popular in\ngenomics and computational biology, due to their good performance in real-world\napplications and strong modularity that makes them suitable to a wide range of\nproblems, from the classification of tumors to the automatic annotation of\nproteins. Their ability to work in high dimension, to process non-vectorial\ndata, and the natural framework they provide to integrate heterogeneous data\nare particularly relevant to various problems arising in computational biology.\nIn this chapter we survey some of the most prominent applications published so\nfar, highlighting the particular developments in kernel methods triggered by\nproblems in biology, and mention a few promising research directions likely to\nexpand in the future.",
    "arxiv_id": "q-bio/0510032",
    "authors": [
      "Jean-Philippe Vert"
    ],
    "categories": [
      "q-bio.QM"
    ],
    "title": "Kernel methods in genomics and computational biology",
    "url": "https://arxiv.org/abs/q-bio/0510032"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "72b8010b-93ae-4a2d-aaec-10d1b2df3b2f",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}