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.


Citation Style Language JSON Export

{
  "DOI": "10.1093/bioinformatics/btaa942", 
  "container_title": "Bioinformatics", 
  "language": "eng", 
  "title": "COSIFER: a Python package for the consensus inference of molecular interaction networks", 
  "issued": {
    "date-parts": [
      [
        2020, 
        11, 
        2
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Manica, M."
    }, 
    {
      "family": "Bunne, C."
    }, 
    {
      "family": "Mathis, R."
    }, 
    {
      "family": "Cadow, J."
    }, 
    {
      "family": "Ahsen, M."
    }, 
    {
      "family": "Stolovitzky"
    }, 
    {
      "family": "Mart\u00ednez, M."
    }
  ], 
  "type": "article-journal", 
  "id": "4748570"
}
72
66
views
downloads
Views 72
Downloads 66
Data volume 19.4 MB
Unique views 68
Unique downloads 65

Share

Cite as