e-TSN: an interactive visual exploration platform for target–disease knowledge mapping from literature

• DOI: 10.1093/bib/bbac465
• Publication Date: 2022-11-09T01:38:55Z
• Authors: Ziyan Feng; Zihao Shen; Honglin Li; Shiliang Li
• Keywords: Identification; Biomedical text mining

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers unstructured texts publications provide a rich source knowledge for drug target research demand development specific algorithms or tools to facilitate finding disease genes proteins. Text mining is method that can automatically mine helpful information related from massive literature. However, there substantial lag between subsequent abstraction extracted text databases. graph introduced integrate heterogeneous Here, we describe e-TSN (Target significance novelty explorer, http://www.lilab-ecust.cn/etsn/), visualization web server integrating largest database associations targets diseases full scientific literature constructing scoring methods based on bibliometric statistics. platform aims visualize target-disease graphs assist in prioritizing candidate disease-related Approved drugs associated bioactivities each interested also provided drug-target relationships. In summary, fast customizable resource investigating analyzing intricate networks, which could help researchers understand mechanisms underlying complex phenotypes improve efficiency, especially unexpected outbreak infectious pandemics like COVID-19.