Table 1.
Reference network influence algorithms. These programs evaluate the driver potential of a mutation based on its effect on a pre-defined reference network.
| Software | Description | Use of Expression Data | Data Type | Primary Language | Year | Ref. |
|---|---|---|---|---|---|---|
| DawnRank | Ranks mutated genes based on connectivity to DEGs using Google’s PageRank algorithm [46]. | Quantitative | Paired- or unpaired- Tumour/ Normal |
R | 2014 | Hou and Ma [36] |
| OncoImpact | Ranks mutated genes based on path length to frequently dysregulated genes and finds minimal set. | Binary | Tumour and reference healthy samples (external) | Perl | 2015 | Bertrand et al. [43] |
| Hit’nDrive | Finds a minimum set of mutated genes with maximal coverage of a user-defined fraction of DEGs | Binary | Tumour Only Data From Collection of Patients | C++ | 2017 | Shrestha et al. [44] |
| Single Sample Controller Strategy (SCS) | Creates basic transition network based on log2 fold-change values and identifies minimal transition network controllers. | Binary | Paired-Tumour/ Normal |
MATLAB | 2018 | Guo et al. [45] |
| Personalised Ranking Of DrIver Genes analYsis (PRODIGY) | Creates confidence-weighted subnetworks including mutated genes and dysregulated pathways and quantifies impact using the prize-collecting Steiner tree (PCST) problem. | Quantitative | Unpaired- Tumour/ Normal |
R | 2020 | Dinstag and Shamir [37] |
| PersonaDrive | Creates bipartite networks of mutated genes connected to DEGs from the same or similar samples, and ranks mutated genes based on the number of pathways in which it connects with a DEG. | Binary | Tumour Only Data From Collection of Patients | Python | 2022 | Erten et al. [46] |