# Overview on Gene Regulatory Network
###### tags: `Bioinformatics`, `Gene Regulatory Network`

## Network Reconstruction Methods
| Year | Name | Data | Method | IR¹ | CR² | S³ |
|---|---|---|---|:---:|:---:|:---:|
| 2006 | _ARACNE_ | • mRNA | • Mutual Information<br>• Remove weakest edge | &#10003; | &#10761; | &#10761; |
| 2007 | _CLR_ | • mRNA | • Mutual Information<br>• $z_{ij}=\sqrt{z_i^2+z_j^2}$| &#10003; | &#10761;| &#10761; |
| 2007 | MRNET | • mRNA | • Mutual Information<br> • MRMR | &#10003; | &#10761; | &#10761; |
| 2007 | LICORN | • mRNA<br>• TF (xprs) | • Discrete Set<br> • Apriori Algorithm | ? | &#10003; | &#10003; |
| 2008 | WGCNA | • mRNA | • Weighted Correlation | &#10761; | &#10761; | &#10761; |
| 2010 | GENIE3 | • mRNA | • Random Forest<br>• Extra Trees | &#10003; | &#10003; | &#10761; |
| 2012 | TIGRESS | • mRNA | • LARS Regresion<br>• Stability Selection | &#10003; | &#10761; | &#10003; |
| 2013 | PANDA | • mRNA<br>• TFBS<br>• PPI<br>• miRNA | • Message Passing | &#10761; | &#10003; | &#10761; |
| 2014 | H-LICORN | • mRNA<br> • TF (xprs) | • Discrete Set<br> • Apriori Algorithm<br> • Linear Regression<br>• Bagging | &#10003; | &#10003; | &#10003; |
| 2014 | _SNF^*_ | • mRNA<br>• Methylation<br>• miRNA | • Message Passing | | |
| 2015 | iRafNet | • mRNA<br>• Matrix | • Random Forest | &#10003; | &#10003; | &#10761; |
| 2015 | _cMonkey2_ | • mRNA<br> • TFBS<br> • Pathway| • K-Means | &#10761; | &#10003; | ? |
| 2015 | COREGNET | • mRNA<br> • TF<br> | • H-LICORN<br>• Data integration | &#10003; | &#10003; | &#10003; |
¹ Models identify indirect regulation or not
² Models identify corregulation of transciption factors or not
³ Sign of edges (&#10003; signed, &#10761;: unsigned)
^* Use message passing methods to identify the tumor subtypes for patients
(doesn't model gene regulatory network)