Table 6.
Summary of studies involved in this review
| Category | Study | Published year | Algorithms | SL data | Feature data | Program code |
|---|---|---|---|---|---|---|
| Statistical-based methods | Li et al. [51] | 2011 | MLE | SGD [130] | Domain relationships | |
| Zhang et al. [52] | 2012 | MLE | SGD [130] | Protein sequences | ||
| Conde-Pueyo et al. [53] | 2009 | Homologous mapping | BioGRID [35–37] | Somatic mutations, GO annotation, drugs and their gene targets | ||
| Lee et al. [54] | 2013 | Homologous mapping | BioGRID [35–37] | Homology information, gene expression information | ||
| Deshpande et al. [55] | 2013 | Homologous mapping | Literatures [56] | Homology information | ||
| Kirzinger et al. [16] | 2019 | Homologous mapping | Gene expression data, homology information | |||
| Jerby-Arnon et al. [13] | 2014 | DAISY | SCNA and mutation profiles, gene essentiality profiles, gene expression profiles | |||
| Srihari et al. [58] | 2015 | Statistical analysis | Genomic copy-number and gene expression | |||
| Guo et al. [34] | 2016 | Statistical analysis | BioGRID [35–37], Syn-Lethality [38] GenomeRNAi [39] DAISY [13] The DECIPHER Project, | http://histone.sce.ntu.edu.sg/SynLethDB/ | ||
| Wang et al. [59] | 2019 | Statistical analysis | SynLethDB [34] and Literatures [15, 49, 58, 61, 131] | Somatic mutation information, shRNA data, yeast genetic interactions | ||
| Lee et al. [60] | 2018 | ISLE | SCNA, gene expression, mutation and survival data | https://github.com/jooslee/ISLE/ | ||
| Wang et al. [61] | 2013 | The univariate F-test or t-test | Gene expression | |||
| Chang et al. [62] | 2016 | Statistical analysis | Literatures [5, 6, 132, 133] | Gene expression | ||
| Feng et al. [63] | 2019 | Statistical analysis | Genomics and patient survival data | |||
| Sinha et al. [65] | 2017 | MiSL | Mutation, copy number and gene expression | https://purl.stanford.edu/ny450yx7231 | ||
| Yang et al. [64] | 2021 | SiLi | Large-scale sequencing data | |||
| Network-based methods | Kranthi et al. [15] | 2013 | PPI networks | PPIs | ||
| Jacunski et al. [14] | 2015 | PPI networks | BioGRID [35–37] | PPIs, functional annotations | ||
| Ku et al. [17] | 2020 | PPI networks | PPIs, pathways | |||
| Zhang et al. [19] | 2015 | Signaling networks | Signaling data | |||
| Liu et al. [18] | 2018 | Signaling networks | SynLethDB [34] | PPIs | ||
| Apaolaza et al. [20] | 2017 | Metabolic networks | Gene expression data | |||
| Megchelenbrink et al. [21] | 2015 | IDLE | The human metabolic network | |||
| Pratapa et al. [22] | 2015 | Fast-SL | Genome-scale metabolic networks | https://github.com/RamanLab/FastSL | ||
| Classic ML methods | Paladugu et al. [67] | 2008 | SVM | Literatures [134] [134–136] | PPI network | |
| Wu et al. [71] | 2021 | k-NN | SynLethDB [34] | Seven similarities of gene pairs (gene expression, protein sequence, PPI, copathway, GO biological process, GO cellular component and GO molecular function) | ||
| Yin et al. [69] | 2019 | DT | SynLethDB [34] | Mutation, CNV and clinical data of breast cancer | ||
| Pandey et al. [72] | 2010 | MNMC | SGD [130] | PPIs, functional annotations, Pathways, mutant phenotype, proteins phylogenetic profiles, sequence similarity of genes and proteins | ||
| Wu et al. [73] | 2014 | Ensemble learning | BioGRID [35–37] | Semantic similarity, PPIs, sequence orthologs, semantic similarity, co-complex membership, co-pathway membership, gene expression correlation, Common/interacting domains, the number of domains | ||
| Das et al. [23] | 2019 | DiscoverSL (RF) | SynLethDB [34] | Mutation, gene expression, copy number alteration, gene-pathway information | https://github.com/shaoli86/DiscoverSL/releases/tag/V1.0 | |
| Li et al. [24] | 2019 | RF | Shen et al. study [44] | GO term and KEGG pathway | ||
| Benstead-Hume et al. [25] | 2019 | RF | BioGRID [35–37] | PPIs | ||
| De Kegel et al. [26] | 2021 | RF | Shared PPIs, evolutionary conservation, etc. | https://github.com/cancergenetics/paralog_SL_prediction; https://doi.org/10.5281/zenodo.5139973 | ||
| Benfatto et al. [27] | PARIS (RF) | CRISPR screens with genomics and transcriptomics data | https://github.com/sbenfatto/PARIS | |||
| Huang et al. [28] | 2019 | GRSMF (Matrix factorization) | SynLethDB [34] | GO similarity matrix | https://github.com/Oyl-CityU/GRSMF | |
| Liany et al. [30] | 2020 | CMF (Matrix factorization) | SynLethDB [34] | Essentiality Profile, mRNA gene expression, SCNA level, pairwise coexpression | https://github.com/lianyh | |
| Liu et al. [29] | 2020 | SL2MF (Matrix factorization | SynLethDB [34] | PPI similarity, GO similarity | ||
| Deep learning methods | Wan et al. [41] | 2020 | Neural network | Shen et al. study [44] GI map [12] Najm et al. study [45] Zhao et al. study [46] | L1000 gene expression profiles [118] | https://github.com/FangpingWan/EXP2SL |
| Cai et al. [31] | 2020 | GCN | SynLethDB [34] | https://github.com/CXX1113/Dual-DropoutGCN | ||
| Long et al. [32] | 2021 | GAT | SynLethDB [34], SynLethDB- v2.0 (http://synlethdb.sist.shanghaitech.edu.cn/v2) | GO semantic similarity, PPIs | https://github.com/longyahui/GCATSL | |
| Hao et al. [33] | 2021 | GAE | SynLethDB [34] | GO similarity matrix, PPIs, coexpression、mutual exclusion score、copathway | https://github.com/DiNg1011/SLMGAE | |
| Zhang et al. [2] | 2021 | KG | SynlethDB [34], Jerby-Arnon et al. [13] | Three relationships (different cancer types and their mutant genes, drugs and targets, drugs and their indications) | ||
| Wang et al. [80] | 2021 | KG | SynLethDB [34], SynLethDB- v2.0 (http://synlethdb.sist.shanghaitech.edu.cn/v2) | The relationships of genes, drugs and compounds |
Note: SVM, support vector machine; DT, Decision tree; k-NN, k-nearest neighbors; RF, random forest; GCN, graph convolutional network; GAT, graph attention network; GAE, graph autoencoder; KG, knowledge graphs; MLE, maximum likelihood estimation; ISLE, identification of clinically relevant synthetic lethality; MiSL, mining synthetic lethals; SiLi, statistical inference-based synthetic lethality identification; IDLE, identifying dosage lethality effects; MNMC, multi-network and multi-classifier; PARIS, PAn-canceR Inferred Synthetic lethalities; GRSMF, graph regularized self-representative matrix factorization; CMF, collective matrix factorization; SGD, saccharomyces genome database; SCNA, somatic copy number alterations.