iENA Version 1
iENA: individual-specific Edge-Network Analysis (iENA) with dynamical network biomarker (DNB) can be used to identify the pre-transition state of each individual in a single-sample manner. In particular, iENA can identify individual-specific biomarkers for the disease prediction, in addition to the traditional disease diagnosis.
Input Parameters
How to cite
[1] Yu X, Zhang J, Sun S, Zhou X, Zeng T, Chen L. Individual-specific edge-network analysis for disease prediction.Nucleic Acids Res. 2017 Nov 16;45(20):e170. doi: 10.1093/nar/gkx787 [PMID=28981699]
[2] Yu X, Zeng T, Wang X, Li G, Chen L. Unravelling personalized dysfunctional gene network of complex diseases based on differential network model. J Transl Med. 2015 Jun 13;13:189. doi: 10.1186/s12967-015-0546-5 [PMID=26070628]
[3] Yu X, Li G, Chen L. Prediction and early diagnosis of complex diseases by edge-network. Bioinformatics. 2014 Mar 15;30(6):852-9. doi: 10.1093/bioinformatics/btt620. Epub 2013 Oct 31 [PMID=24177717]
Help information
iENA

perl SSN.pl -data -ref -net -format_out1 -format_out2
opts:
-data    Predicted file
-ref       Reference net file
-net      Information of related genes
-format_out1    Output format options (1 stands the above third files can be empty, 0 stands else)
-format_out2    Output format options (1 stands edge quantification data; 0 stands node quantification data)

Parameters Description
 -Data: Predicted file
predicted file
 -Ref: Reference file
reference file
 -Net: Information of related genes
information of related genes
 -F1: format_out1
1 stands the above third files can be empty, 0 stands else
 -F2: format_out2
1 stands the above third files can be empty, 0 stands else
  • Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No. XDB13000000
    Maintained by BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences.