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Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information.

Xiao-Nan FanShao-Wu ZhangSong-Yao ZhangKunju ZhuSongjian Lu
Published in: BMC bioinformatics (2019)
Compared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://github.com/NWPU-903PR/IDHI-MIRW .
Keyphrases
  • long non coding rna
  • long noncoding rna
  • papillary thyroid
  • health information
  • cell proliferation
  • deep learning
  • squamous cell carcinoma
  • drinking water
  • squamous cell
  • free survival
  • lymph node metastasis