Table 3.
Summary of some studies involved in this review
| Study | Published year | Algorithms | Category of methods | Drug combination data set | Input data types | Program code |
|---|---|---|---|---|---|---|
| Tang et al. [130] | 2013 | TIMMA | Systems biology methods | Experiments | Target profiles | https://cran.r-project.org/web/packages/timma/index.html [158] |
| Pivetta et al. [93] | 2013 | ANN | Classic ML methods | Experiments | Concentrations of drugs | |
| Chen et al. [98] | 2013 | RF | Classic ML methods | Zhao et al. [23] | DDIs, PPIs, targets-enriched pathways | |
| Sun et al. [90] | 2014 | One-class SVM | Classic ML methods | DCDB | Gene expression profiles | |
| Huang et al. [132] | 2014 | LR | Classic ML methods | DCDB | Side effects | |
| Li et al. [159] | 2015 | PEA | Systems biology methods | DCDB, TTD, Literatures | Fingerprints, ATC codes, side effects, targets’ sequences, PPIs, targets’ GO terms | |
| Sun et al. [38] | 2015 | RACS | Systems biology methods | DCDB, Literatures, NCI-DREAM, TTD | GO-based mutual information entropy, topological features in drug/target networks | https://github.com/DrugCombination/RACS |
| Wildenhain et al. [97] | 2015 | SONAR | Classic ML methods | Experiments. | Chemical-genetic interactions, fingerprints | |
| Chen et al. [39] | 2016 | NLLSS | Systems biology methods | Literatures | Structural information, DTIs | |
| Gayvert et al. [81] | 2017 | RF | Classic ML methods | Held et al. [160] study. | Single drug dose response | |
| Li et al. [100] | 2017 | RF | Classic ML methods | AZ-DREAM | Structural information, target networks, drug induced gene expression data | |
| Xu et al. [82] | 2017 | SGB | Classic ML methods | DCDB | Fingerprints, ATC codes, PPIs, CCIs, and disease pathways. | |
| Shi et al. [108] | 2017 | TLMCS | Classic ML methods | DCDB | ATC codes, DDIs, DTIs, targets’ GO terms, SEs | |
| Shi et al. [133] | 2017 | LR, ensemble learning | Classic ML methods | DCDB | DDIs, DTIs, SEs, ATC codes | https://github.com/JustinShi2016/Drug-Drug-Interactions/tree/master/ISBRA2016 |
| KalantarMotamedi et al. [99] | 2018 | RF | Classic ML methods | NCATS [161] | Targets, targets’ pathways | |
| Preuer et al. [20] | 2018 | DeepSynergy | Deep learning methods | O’neil et al. study. | Fingerprints, physicochemical, toxicophore features, cell lines’ gene expression levels | www.bioinf.jku.at/software/DeepSynergy |
| Janizek et al. [80] | 2018 | TreeCombo | Classic ML methods | O’Neil et al. study. | Fingerprints, toxicophore structures, cell lines’ gene expression levels | |
| He et al. [102] | 2018 | DCPT | Classic ML methods | Experiments | Exome-seq, RNA-seq and target profiles | |
| Chen et al. [116] | 2018 | DBN | Deep learning methods | AZ-DREAM | Ontology fingerprints, cell lines’ gene expression, targets’ pathways | |
| Cheng et al. [129] | 2019 | Proximity | Systems biology methods | DCDB, TTD | PPIs | https://github.com/emreg00/toolbox |
| Liu et al. [136] | 2019 | RWR, GTB | Classic ML methods | DCDB | DTIs, CCIs, targets’ sequences, targets’ GOs | https://github.com/hliu2016/SynerDrug |
| Sidorov et al. [36] | 2019 | RF, XGBoost | Classic ML methods | NCI-ALMANAC | structure features, physicochemical properties | http://ballester.marseille.inserm.fr/NCI-Alm-Predictors.zip |
| Andrew et al. [103] | 2019 | RF | Classic ML methods | Commercial data. | Clinical trial features | |
| Lanevski et al. [18] | 2019 | DECREASE | Classic ML methods | Experiments, O’Neil et al. study. | Dose–response matrix |
http://decrease.fimm.fi
https://github.com/IanevskiAleksandr/DECREASE/tree/master/210_Novel_Anticancer_combinations |
| Zhang et al. [137] | 2019 | FFM | Classic ML methods | DCDB, NData [2] | Targets, enzymes, ATC codes, | |
| Julkunen et al. [94] | 2020 | comboFM | Classic ML methods | NCI-ALMANAC, etc. | Fingerprints, cell lines’ gene expression, drugs’ concentrations | https://doi.org/10.5281/zenodo.4129688 |
| Jiang et al. [121] | 2020 | GCN | Deep learning methods | O’Neil et al. Study | PPIs, DTIs | |
| Kuru et al. | 2021 | MatchMaker | Deep learning methods | DrugComb. | Structural and physiochemical, cells’ gene expression | |
| Zhang et al. [117] | 2021 | AuDNNsynergy | Deep learning methods | O’neil et al. study | Fingerprints, physicochemical properties, cell lines’ gene expression, mutation, copy number variation |
Notes: TIMMA: Target inhibition interaction using maximization and minimization averaging. PEA: Probability ensemble approach. RACS: Ranking-system of anti-cancer synergy. NLLSS: Network-based Laplacian regularized least-square synergistic drug combination prediction. GTB: Gradient tree boosting. cNMF: Composite non-negative matrix factorization. FFM: Field-aware factorization machines. FM: Factorization machine.