Journal article Open Access

COSIFER: a Python package for the consensus inference of molecular interaction networks

Manica, M.; Bunne, C.; Mathis, R.; Cadow, J.; Ahsen, M.; Stolovitzky; Martínez, M.


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/2fc2c56a-7b16-463e-a90a-65085e70f4c6/btaa942.pdf"
      }, 
      "checksum": "md5:5f4930e120d35a507a2ce4cc88f7728b", 
      "bucket": "2fc2c56a-7b16-463e-a90a-65085e70f4c6", 
      "key": "btaa942.pdf", 
      "type": "pdf", 
      "size": 294183
    }
  ], 
  "owners": [
    65392
  ], 
  "doi": "10.1093/bioinformatics/btaa942", 
  "stats": {
    "version_unique_downloads": 65.0, 
    "unique_views": 68.0, 
    "views": 72.0, 
    "version_views": 72.0, 
    "unique_downloads": 65.0, 
    "version_unique_views": 68.0, 
    "volume": 19416078.0, 
    "version_downloads": 66.0, 
    "downloads": 66.0, 
    "version_volume": 19416078.0
  }, 
  "links": {
    "doi": "https://doi.org/10.1093/bioinformatics/btaa942", 
    "latest_html": "https://zenodo.org/record/4748570", 
    "bucket": "https://zenodo.org/api/files/2fc2c56a-7b16-463e-a90a-65085e70f4c6", 
    "badge": "https://zenodo.org/badge/doi/10.1093/bioinformatics/btaa942.svg", 
    "html": "https://zenodo.org/record/4748570", 
    "latest": "https://zenodo.org/api/records/4748570"
  }, 
  "created": "2021-05-11T09:29:03.649815+00:00", 
  "updated": "2021-05-12T13:48:12.844037+00:00", 
  "conceptrecid": "4748569", 
  "revision": 2, 
  "id": 4748570, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.1093/bioinformatics/btaa942", 
    "description": "<p>The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks.<br>\n&nbsp;</p>", 
    "language": "eng", 
    "title": "COSIFER: a Python package for the consensus inference of molecular interaction networks", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "journal": {
      "title": "Bioinformatics"
    }, 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "4748569"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "4748570"
          }
        }
      ]
    }, 
    "grants": [
      {
        "code": "826121", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::826121"
        }, 
        "title": "individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology", 
        "acronym": "iPC", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }
    ], 
    "communities": [
      {
        "id": "ipc"
      }
    ], 
    "publication_date": "2020-11-02", 
    "creators": [
      {
        "affiliation": "IBM Research Europe", 
        "name": "Manica, M."
      }, 
      {
        "affiliation": "IBM Research Europe", 
        "name": "Bunne, C."
      }, 
      {
        "affiliation": "IBM Research Europe", 
        "name": "Mathis, R."
      }, 
      {
        "affiliation": "IBM Research Europe", 
        "name": "Cadow, J."
      }, 
      {
        "affiliation": "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai", 
        "name": "Ahsen, M."
      }, 
      {
        "affiliation": "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai", 
        "name": "Stolovitzky"
      }, 
      {
        "affiliation": "IBM Research Europe", 
        "name": "Mart\u00ednez, M."
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "subtype": "article", 
      "type": "publication", 
      "title": "Journal article"
    }
  }
}
72
66
views
downloads
Views 72
Downloads 66
Data volume 19.4 MB
Unique views 68
Unique downloads 65

Share

Cite as