{
  "annotation_id": "ea4204e3-695e-4837-883f-f52107a9b0e5",
  "date_created": "2026-03-02T18:01:31.855000Z",
  "date_modified": "2026-03-02T18:01:31.855000Z",
  "file_hash": "2181f7b565f25f3f95e974be1a97304ca8eceffe7f1788dbee7810c5b67e67a3",
  "private": false,
  "record": {
    "abstract": "Gene expression data matrices often contain missing expression values. In\nthis paper, we describe a new algorithm, named improved fixed rank\napproximation algorithm (IFRAA), for missing values estimations of the large\ngene expression data matrices. We compare the present algorithm with the two\nexisting and widely used methods for reconstructing missing entries for DNA\nmicroarray gene expression data: the Bayesian principal component analysis\n(BPCA) and the local least squares imputation method (LLS). The three\nalgorithms were applied to four microarray data sets and two synthetic low-rank\ndata matrices. Certain percentages of the elements of these data sets were\nrandomly deleted, and the three algorithms were used to recover them. In\nconclusion IFRAA appears to be the most reliable and accurate approach for\nrecovering missing DNA microarray gene expression data, or any other noisy data\nmatrices that are effectively low rank.",
    "arxiv_id": "q-bio/0510047",
    "authors": [
      "Shmuel Friedland",
      "Mostafa Kaveh",
      "Amir Niknejad",
      "Hossein Zare"
    ],
    "categories": [
      "q-bio.GN",
      "math.NA"
    ],
    "title": "An Algorithm for Missing Value Estimation for DNA Microarray Data",
    "url": "https://arxiv.org/abs/q-bio/0510047"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c6a4c123-92a1-4854-8bd3-296aa69d65b3",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}