Predicting lncrna-protein interactions with bipartite graph embedding and deep graph neural networks

HIGHLIGHTS

• who: . et al. from the Shandong University, China have published the research work: Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks, in the Journal: (JOURNAL)
• what: With the model iterations in GNN, existing research focuses on convolutions in graph data mining. Inspired by this work, the authors design the feature of lncRNA and protein nodes. The authors compare the proposed BiHo-GNN framework with five methods, including LPIGAC (Jin et_al, 2021), LncPNet (Zhao et_al, 2022), RWR (Random Walk with Restart) (Wiggins et_al, 2016) and LPBNI (Ge et_al, 2016) on NPInter2 . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.