GIP: A Gene network-based integrative approach for Inferring disease-associated signaling Pathways

Dysregulation or crosstalk of signal transduction pathways contributes to disease development. Despite the initial success of identifying causal links between source and target proteins in simple or well-studied biological systems, it remains challenging to investigate alternative pathways specifically associated with a disease. We develop a Gene network-based integrative approach for Inferring disease-associated signaling Pathways (GIP). Specifically, we identify alternative pathways given source and target proteins. GIP was applied to human breast cancer data. Experimental results showed that GIP identified biologically meaningful pathway modules associated with antiestrogen resistance.

• Publication year

  2019

• Venue

  bioRxiv

• Publication date

  2019-05-31

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1101/654780
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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