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The Predicted EF-miRNA-Disease Associations Database

MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which leading to the progress of many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. Here, we developed a computational systems toxicology framework, namely the PEMDAM, to predict new associations among EFs, miRNAs and diseases by integrating EF structural similarity and disease phenotypic similarity. Three comprehensive bipartite networks, EF-miRNA association (EMA), EF-disease association (EDA) and miRNA-disease association (MDA), were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. We generated a comprehensive prediction list, the PEMDAM lists, including all of the potential MDAs, EDAs and MDAs found by our computational program. Researchers interested in EF-miRNA-disease associations can download the profile for further experimental validation. Our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.

The PEMDAM lists    (Download)

This profile includes:

Experimental Data sets:

  • EF-Disease Associations (EDA) Dataset
  • EF-miRNA Associations (EMA) Dataset
  • miRNA-Disease Associations (MDA) Dataset

Predicted EF-miRNA-Disease Associations:

  • Predicted Association Lists for EFs
  • Predicted Association Lists for miRNAs
  • Predicted Association Lists for Diseases



Jie Li, Zengrui Wu, Feixiong Cheng*, Weihua Li, Guixia Liu, Yun Tang*. Computational Prediction of microRNA Networks Incorporating Environmental Toxicity and Disease Etiology. Sci Rep. 2014, 4: 5576.