Computational methods, databases and tools for synthetic lethality prediction

Abstract Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP in...

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Published in:Briefings in bioinformatics Vol. 23; no. 3
Main Authors: Wang, Jing, Zhang, Qinglong, Han, Junshan, Zhao, Yanpeng, Zhao, Caiyun, Yan, Bowei, Dai, Chong, Wu, Lianlian, Wen, Yuqi, Zhang, Yixin, Leng, Dongjin, Wang, Zhongming, Yang, Xiaoxi, He, Song, Bo, Xiaochen
Format: Journal Article
Language:English
Published: England Oxford University Press 13.05.2022
Oxford Publishing Limited (England)
Subjects:
Cancer
Cell death
Cell survival
Computer applications
Computer networks
Deactivation
Deep learning
Genes
Inactivation
Lethality
Machine learning
Poly(ADP-ribose) polymerase
Predictions
Review
Sampling methods
Software
Statistical methods
computational methods
deep learning
machine learning
synthetic lethality
ISSN:1467-5463, 1477-4054, 1477-4054
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Abstract Abstract Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
AbstractList Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
Abstract Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
Author Wen, Yuqi
Zhao, Caiyun
Leng, Dongjin
Zhang, Qinglong
Bo, Xiaochen
Wu, Lianlian
Wang, Jing
Yan, Bowei
He, Song
Han, Junshan
Zhao, Yanpeng
Wang, Zhongming
Yang, Xiaoxi
Dai, Chong
Zhang, Yixin
AuthorAffiliation Department of Bioinformatics, Institute of Health Service and Transfusion Medicine , Beijing 100850, China
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  email: boxiaoc@163.com
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Cites_doi 10.1158/2159-8290.CD-20-1503
10.1093/nar/gkv452
10.1038/s41467-017-00555-y
10.1093/nar/gki107
10.1186/1752-0509-5-73
10.1073/pnas.1900654116
10.1093/nar/26.1.73
10.1128/AAC.01319-19
10.1016/j.omtn.2019.07.003
10.1016/j.cell.2017.06.010
10.1186/s13148-019-0734-x
10.1371/journal.pcbi.1006888
10.1016/j.cell.2017.10.049
10.1093/nar/gkaa1074
10.1038/nature08460
10.1093/bioinformatics/bty673
10.1093/nar/gkj123
10.1093/nar/gky1079
10.1038/s41586-019-1102-x
10.1186/jbiol36
10.1073/pnas.1706139114
10.1038/srep16025
10.1126/sciadv.abc2100
10.1371/journal.pcbi.1003637
10.3390/cancers11111682
10.1038/nature11003
10.1038/nature03443
10.1093/bioinformatics/btq053
10.1093/nar/gkw985
10.1038/nbt1096
10.18632/oncotarget.12046
10.1126/science.1091317
10.1038/nmeth.4627
10.3389/fphar.2020.00112
10.1038/msb.2013.54
10.1016/j.cell.2021.03.030
10.1039/c3mb25589a
10.1126/science.aam7344
10.1083/jcb.202006159
10.1371/journal.pcbi.1004506
10.1016/j.chembiol.2021.01.026
10.1186/1477-5956-10-S1-S4
10.1093/bib/bbab036
10.1016/j.cels.2016.02.003
10.1038/nmeth.4225
10.1109/TCBB.2019.2909908
10.1073/pnas.1508573112
10.1371/journal.pcbi.1000928
10.1038/s41571-018-0055-6
10.1093/nar/gkaa1023
10.1126/science.aao1729
10.1002/pro.3978
10.1016/j.ccell.2019.01.009
10.1038/sj.bjc.6601894
10.1002/jcb.27395
10.1186/s13059-019-1745-9
10.1038/s41467-020-16078-y
10.1093/nar/gks1170
10.1038/nmeth.2436
10.1016/j.ccell.2020.09.014
10.1186/s12920-019-0554-z
10.1093/nar/gkn892
10.1038/nbt.4048
10.1038/s41586-019-1103-9
10.1016/j.cell.2009.05.006
10.1016/j.cell.2018.06.010
10.1038/nrg.2017.47
10.1186/s13062-015-0086-1
10.1098/rsif.2017.0387
10.1093/bib/bbab128
10.1016/j.cels.2021.08.006
10.1016/j.trecan.2018.08.003
10.7150/thno.63416
10.1186/s12859-019-3197-3
10.1186/1752-0509-3-116
10.1093/nar/gky973
10.1093/bioinformatics/btv352
10.1016/j.csbj.2020.04.012
10.1038/nchembio.1967
10.1158/1940-6207.CAPR-10-0235
10.1126/science.1180823
10.1186/s12943-021-01405-8
10.1007/978-1-4419-7046-6_19
10.1093/nar/gkab338
10.1613/jair.953
10.1038/s41467-021-21544-2
10.1016/j.cell.2014.07.027
10.1007/s11390-021-0866-2
10.1016/j.ijpddr.2013.06.001
10.1128/AAC.01533-17
10.1038/s42256-019-0138-9
10.1038/s41392-020-00358-6
10.1016/j.cels.2015.12.004
10.1093/nar/gkv1108
10.1038/ncomms15580
10.18632/oncotarget.10536
10.1109/JBHI.2021.3079302
10.1016/j.molcel.2018.01.017
10.1186/1471-2105-9-426
10.1093/nar/gky868
10.1038/s41598-018-26783-w
10.1093/nar/gkaa1100
10.1093/bioinformatics/btab271
10.1038/s41467-018-04647-1
10.1093/nar/gkw1092
10.1126/science.aaf1420
10.1016/j.trecan.2018.11.001
10.1038/s41573-019-0046-z
10.1093/bioinformatics/btz893
10.1158/0008-5472.CAN-12-3956
10.1093/nar/gkj109
10.1038/nature03445
10.1080/00031305.1992.10475879
10.1186/1755-8794-6-30
10.1093/bioinformatics/btab110
10.1016/j.celrep.2019.06.067
10.1186/s13045-020-00956-5
10.1142/S0219720015410024
10.1093/bioinformatics/btaa211
10.1093/nar/gky1055
10.1016/j.molcel.2004.09.035
10.1016/j.molcel.2016.06.022
10.1093/bib/bbx172
10.1038/s41598-020-60574-6
10.1109/TPAMI.2005.159
10.1126/sciadv.abg2106
10.1053/j.seminoncol.2015.09.015
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Issue 3
Keywords computational methods
deep learning
machine learning
synthetic lethality
Language English
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Jing Wang and Qinglong Zhang authors contributed equally to this work.
ORCID 0000-0002-9611-4488
0000-0003-1911-7922
0000-0002-4136-6151
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  day: 13
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PublicationTitle Briefings in bioinformatics
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References Zhang (2022051813464665800_ref19) 2015; 13
The Gene Ontology C (2022051813464665800_ref114) 2019; 47
Li (2022051813464665800_ref9) 2020; 5
Giurgiu (2022051813464665800_ref120) 2019; 47
Sayers (2022051813464665800_ref112) 2021; 49
Bryant (2022051813464665800_ref5) 2005; 434
Jerby-Arnon (2022051813464665800_ref13) 2014; 158
Srivastava (2022051813464665800_ref77) 2014; 15
Cheng (2022051813464665800_ref102) 2021; 7
Hao (2022051813464665800_ref33) 2021; 25
Li (2022051813464665800_ref38) 2014; 2014
Wu (2022051813464665800_ref73) 2014; 13
Yang (2022051813464665800_ref93) 2019; 11
Hu (2022051813464665800_ref50) 2021; 22
Grigoroiu (2022051813464665800_ref66) 2020; 10
Feng (2022051813464665800_ref63) 2019; 35
Akimov (2022051813464665800_ref89) 2021; 28
Luo (2022051813464665800_ref133) 2009; 137
Yu (2022051813464665800_ref111) 2016; 2
Horlbeck (2022051813464665800_ref12) 2018; 174
Lundberg (2022051813464665800_ref76) 2020; 2
Wu (2022051813464665800_ref71) 2021; 36
Ho (2022051813464665800_ref74) 1995
Keshava Prasad (2022051813464665800_ref122) 2009; 37
Stark (2022051813464665800_ref37) 2006; 34
Pasquina (2022051813464665800_ref96) 2016; 12
Ma (2022051813464665800_ref110) 2018; 15
Guell (2022051813464665800_ref101) 2014; 10
Oughtred (2022051813464665800_ref36) 2019; 47
Conde-Pueyo (2022051813464665800_ref53) 2009; 3
Farmer (2022051813464665800_ref6) 2005; 434
Barretina (2022051813464665800_ref125) 2012; 483
Xiao (2022051813464665800_ref97) 2019; 63
Altman (2022051813464665800_ref70) 1992; 46
Yin (2022051813464665800_ref69) 2019
Liu (2022051813464665800_ref18) 2018; 8
Srihari (2022051813464665800_ref58) 2015; 10
Murdoch (2022051813464665800_ref109) 2019; 116
Uniprot (2022051813464665800_ref113) 2021; 49
Laufer (2022051813464665800_ref42) 2013; 10
Kuenzi (2022051813464665800_ref108) 2020; 38
Yu (2022051813464665800_ref92) 2021; 7
Kuzmin (2022051813464665800_ref86) 2018; 360
Kirzinger (2022051813464665800_ref16) 2019; 12
Setton (2022051813464665800_ref4) 2021; 11
Kanehisa (2022051813464665800_ref115) 2017; 45
Schmidt (2022051813464665800_ref39) 2013; 41
Costanzo (2022051813464665800_ref56) 2010; 327
Wang (2022051813464665800_ref80) 2021; 37
Deshpande (2022051813464665800_ref55) 2013; 73
Gu (2022051813464665800_ref88) 2018; 19
Costanzo (2022051813464665800_ref40) 2016; 353
Zamanighomi (2022051813464665800_ref47) 2019; 20
Vizeacoumar (2022051813464665800_ref43) 2013; 9
Chan (2022051813464665800_ref91) 2019; 568
Apaolaza (2022051813464665800_ref20) 2017; 8
Barbie (2022051813464665800_ref132) 2009; 462
De Kegel (2022051813464665800_ref26) 2021; 12
Zhang (2022051813464665800_ref2) 2021; 12
Megchelenbrink (2022051813464665800_ref21) 2015; 112
Paladugu (2022051813464665800_ref67) 2008; 9
Ryan (2022051813464665800_ref87) 2018; 4
O'neil (2022051813464665800_ref10) 2017; 18
Velikovi (2022051813464665800_ref78)
Lord (2022051813464665800_ref7) 2017; 355
Liu (2022051813464665800_ref29) 2020; 17
Srivas (2022051813464665800_ref49) 2016; 63
Pan (2022051813464665800_ref135) 2004; 16
Subramanian (2022051813464665800_ref118) 2017; 171
Alanis-Lobato (2022051813464665800_ref123) 2017; 45
Mast (2022051813464665800_ref99) 2020; 219
Walcott (2022051813464665800_ref104) 2016; 43
Sinha (2022051813464665800_ref65) 2017; 8
Tsherniak (2022051813464665800_ref124) 2017; 170
Chen (2022051813464665800_ref128) 2006; 34
Shen (2022051813464665800_ref44) 2017; 14
Kelley (2022051813464665800_ref57) 2005; 23
Oughtred (2022051813464665800_ref35) 2021; 30
Wappett (2022051813464665800_ref85) 2021; 49
Behan (2022051813464665800_ref90) 2019; 568
Cai (2022051813464665800_ref31) 2020; 36
Wan (2022051813464665800_ref41) 2020; 11
Li (2022051813464665800_ref51) 2011; 5
Chawla (2022051813464665800_ref107) 2002; 16
Peng (2022051813464665800_ref75) 2005; 27
Ching (2022051813464665800_ref81) 2018; 15
Das (2022051813464665800_ref23) 2019; 35
Che (2022051813464665800_ref68) 2011; 696
Magen (2022051813464665800_ref84) 2019; 28
Kalia (2022051813464665800_ref95) 2017; 114
Subramaniam (2022051813464665800_ref98) 2018; 62
Long (2022051813464665800_ref32) 2021; 37
Zhang (2022051813464665800_ref52) 2012; 10
Lippert (2022051813464665800_ref100) 2010; 26
Aziz (2022051813464665800_ref94) 2015; 5
Ku (2022051813464665800_ref17) 2020; 11
Jacunski (2022051813464665800_ref14) 2015; 11
Benfatto (2022051813464665800_ref27) 2021; 20
Huang (2022051813464665800_ref28) 2019; 20
Wang (2022051813464665800_ref61) 2013; 6
Chang (2022051813464665800_ref62) 2016; 7
Almozlino (2022051813464665800_ref82) 2020; 18
Bamford (2022051813464665800_ref126) 2004; 91
Topatana (2022051813464665800_ref8) 2020; 13
Deng (2022051813464665800_ref83) 2019; 11
Guo (2022051813464665800_ref34) 2016; 44
Szklarczyk (2022051813464665800_ref121) 2021; 49
Lee (2022051813464665800_ref105) 2021; 184
Huang (2022051813464665800_ref1) 2020; 19
Aromolaran (2022051813464665800_ref106) 2021; 22
Sadreyev (2022051813464665800_ref119) 2015; 43
Kranthi (2022051813464665800_ref15) 2013; 9
Liberzon (2022051813464665800_ref116) 2015; 1
Lee (2022051813464665800_ref54) 2013; 3
Reguly (2022051813464665800_ref134) 2006; 5
Yang (2022051813464665800_ref64) 2021; 11
Cherry (2022051813464665800_ref130) 1998; 26
Tong (2022051813464665800_ref136) 2004; 303
Liany (2022051813464665800_ref30) 2020; 36
Nováček (2022051813464665800_ref79) 2020; 2020
Pandey (2022051813464665800_ref72) 2010; 6
Pratapa (2022051813464665800_ref22) 2015; 31
Ye (2022051813464665800_ref131) 2016; 7
Lee (2022051813464665800_ref60) 2018; 9
Wang (2022051813464665800_ref59) 2019; 20
O'brien (2022051813464665800_ref127) 2005; 33
Han (2022051813464665800_ref48) 2019; 17
Davis (2022051813464665800_ref117) 2019; 47
Parameswaran (2022051813464665800_ref11) 2019; 5
Benstead-Hume (2022051813464665800_ref25) 2019; 15
Najm (2022051813464665800_ref45) 2018; 36
Ashworth (2022051813464665800_ref3) 2018; 15
Fischer (2022051813464665800_ref129) 2011; Chapter 6
Li (2022051813464665800_ref24) 2019; 120
Huang (2022051813464665800_ref103) 2011; 4
Zhao (2022051813464665800_ref46) 2018; 69
References_xml – volume: 11
  start-page: 1626
  issue: 7
  year: 2021
  ident: 2022051813464665800_ref4
  article-title: Synthetic lethality in cancer therapeutics: the next generation
  publication-title: Cancer Discov
  doi: 10.1158/2159-8290.CD-20-1503
– volume: 43
  start-page: W154
  issue: W1
  year: 2015
  ident: 2022051813464665800_ref119
  article-title: PhyloGene server for identification and visualization of co-evolving proteins using normalized phylogenetic profiles
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkv452
– volume: 8
  start-page: 459
  issue: 1
  year: 2017
  ident: 2022051813464665800_ref20
  article-title: An in-silico approach to predict and exploit synthetic lethality in cancer metabolism
  publication-title: Nat Commun
  doi: 10.1038/s41467-017-00555-y
– volume: 33
  start-page: D476
  issue: Database issue
  year: 2005
  ident: 2022051813464665800_ref127
  article-title: Inparanoid: a comprehensive database of eukaryotic orthologs
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gki107
– volume: 5
  start-page: 73
  year: 2011
  ident: 2022051813464665800_ref51
  article-title: Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions
  publication-title: BMC Syst Biol
  doi: 10.1186/1752-0509-5-73
– volume: 116
  start-page: 22071
  issue: 44
  year: 2019
  ident: 2022051813464665800_ref109
  article-title: Definitions, methods, and applications in interpretable machine learning
  publication-title: Proc Natl Acad Sci U S A
  doi: 10.1073/pnas.1900654116
– volume: 26
  start-page: 73
  issue: 1
  year: 1998
  ident: 2022051813464665800_ref130
  article-title: SGD: saccharomyces genome database
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/26.1.73
– volume: 63
  start-page: e01319
  issue: 11
  year: 2019
  ident: 2022051813464665800_ref97
  article-title: Revisiting the beta-lactams for tuberculosis therapy with a compound-compound synthetic lethality approach
  publication-title: Antimicrob Agents Chemother
  doi: 10.1128/AAC.01319-19
– volume: 17
  start-page: 688
  year: 2019
  ident: 2022051813464665800_ref48
  article-title: Genetic interaction-based biomarkers identification for drug resistance and sensitivity in cancer cells
  publication-title: Mol Ther Nucleic Acids
  doi: 10.1016/j.omtn.2019.07.003
– volume: 170
  start-page: 564
  issue: 3
  year: 2017
  ident: 2022051813464665800_ref124
  article-title: Defining a cancer dependency map
  publication-title: Cell
  doi: 10.1016/j.cell.2017.06.010
– volume: 11
  start-page: 136
  issue: 1
  year: 2019
  ident: 2022051813464665800_ref93
  article-title: Epigenetic synthetic lethality approaches in cancer therapy
  publication-title: Clin Epigenetics
  doi: 10.1186/s13148-019-0734-x
– volume: 15
  start-page: e1006888
  issue: 4
  year: 2019
  ident: 2022051813464665800_ref25
  article-title: Predicting synthetic lethal interactions using conserved patterns in protein interaction networks
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1006888
– volume: 171
  start-page: 1437
  issue: 6
  year: 2017
  ident: 2022051813464665800_ref118
  article-title: A next generation connectivity map: L1000 platform and the first 1 000 000 profiles
  publication-title: Cell
  doi: 10.1016/j.cell.2017.10.049
– volume: 49
  start-page: D605
  issue: D1
  year: 2021
  ident: 2022051813464665800_ref121
  article-title: The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkaa1074
– volume: 462
  start-page: 108
  issue: 7269
  year: 2009
  ident: 2022051813464665800_ref132
  article-title: Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1
  publication-title: Nature
  doi: 10.1038/nature08460
– volume: 35
  start-page: 701
  issue: 4
  year: 2019
  ident: 2022051813464665800_ref23
  article-title: DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/bty673
– volume: 34
  start-page: D363
  issue: Database issue
  year: 2006
  ident: 2022051813464665800_ref128
  article-title: OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkj123
– volume: 47
  start-page: D529
  issue: D1
  year: 2019
  ident: 2022051813464665800_ref36
  article-title: The BioGRID interaction database: 2019 update
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gky1079
– volume: 568
  start-page: 551
  issue: 7753
  year: 2019
  ident: 2022051813464665800_ref91
  article-title: WRN helicase is a synthetic lethal target in microsatellite unstable cancers
  publication-title: Nature
  doi: 10.1038/s41586-019-1102-x
– volume: 5
  start-page: 11
  issue: 4
  year: 2006
  ident: 2022051813464665800_ref134
  article-title: Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae
  publication-title: J Biol
  doi: 10.1186/jbiol36
– volume: 114
  start-page: 7426
  issue: 28
  year: 2017
  ident: 2022051813464665800_ref95
  article-title: Exploiting the synthetic lethality between terminal respiratory oxidases to kill mycobacterium tuberculosis and clear host infection
  publication-title: Proc Natl Acad Sci U S A
  doi: 10.1073/pnas.1706139114
– volume: 5
  start-page: 16025
  year: 2015
  ident: 2022051813464665800_ref94
  article-title: Systems biology-guided identification of synthetic lethal gene pairs and its potential use to discover antibiotic combinations
  publication-title: Sci Rep
  doi: 10.1038/srep16025
– volume: 7
  start-page: e1003637
  issue: 1
  year: 2021
  ident: 2022051813464665800_ref102
  article-title: Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity
  publication-title: Sci Adv
  doi: 10.1126/sciadv.abc2100
– volume: 10
  start-page: e1003637
  issue: 5
  year: 2014
  ident: 2022051813464665800_ref101
  article-title: Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1003637
– volume: 11
  start-page: 1682
  issue: 11
  year: 2019
  ident: 2022051813464665800_ref83
  article-title: SL-BioDP: multi-cancer interactive tool for prediction of synthetic lethality and response to cancer treatment
  publication-title: Cancers (Basel)
  doi: 10.3390/cancers11111682
– volume: 483
  start-page: 603
  issue: 7391
  year: 2012
  ident: 2022051813464665800_ref125
  article-title: The cancer cell line Encyclopedia enables predictive modelling of anticancer drug sensitivity
  publication-title: Nature
  doi: 10.1038/nature11003
– volume: 434
  start-page: 913
  issue: 7035
  year: 2005
  ident: 2022051813464665800_ref5
  article-title: Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase
  publication-title: Nature
  doi: 10.1038/nature03443
– volume: 26
  start-page: 912
  issue: 7
  year: 2010
  ident: 2022051813464665800_ref100
  article-title: Gene function prediction from synthetic lethality networks via ranking on demand
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btq053
– volume: 13
  start-page: 71
  issue: Suppl 3
  year: 2014
  ident: 2022051813464665800_ref73
  article-title: In silico prediction of synthetic lethality by meta-analysis of genetic interactions, functions, and pathways in yeast and human cancer
  publication-title: Cancer Inform
– volume: 45
  start-page: D408
  issue: D1
  year: 2017
  ident: 2022051813464665800_ref123
  article-title: HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkw985
– volume: 2014
  start-page: 196034
  year: 2014
  ident: 2022051813464665800_ref38
  article-title: Syn-lethality: an integrative knowledge base of synthetic lethality towards discovery of selective anticancer therapies
  publication-title: Biomed Res Int
– volume: 23
  start-page: 561
  issue: 5
  year: 2005
  ident: 2022051813464665800_ref57
  article-title: Systematic interpretation of genetic interactions using protein networks
  publication-title: Nat Biotechnol
  doi: 10.1038/nbt1096
– volume: 7
  start-page: 73664
  issue: 45
  year: 2016
  ident: 2022051813464665800_ref62
  article-title: Uncovering synthetic lethal interactions for therapeutic targets and predictive markers in lung adenocarcinoma
  publication-title: Oncotarget
  doi: 10.18632/oncotarget.12046
– volume: 303
  start-page: 808
  issue: 5659
  year: 2004
  ident: 2022051813464665800_ref136
  article-title: Global mapping of the yeast genetic interaction network
  publication-title: Science
  doi: 10.1126/science.1091317
– volume: 15
  start-page: 290
  issue: 4
  year: 2018
  ident: 2022051813464665800_ref110
  article-title: Using deep learning to model the hierarchical structure and function of a cell
  publication-title: Nat Methods
  doi: 10.1038/nmeth.4627
– volume: 11
  start-page: 112
  year: 2020
  ident: 2022051813464665800_ref41
  article-title: EXP2SL: a machine learning framework for cell-line-specific synthetic lethality prediction
  publication-title: Front Pharmacol
  doi: 10.3389/fphar.2020.00112
– volume: 9
  start-page: 696
  year: 2013
  ident: 2022051813464665800_ref43
  article-title: A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities
  publication-title: Mol Syst Biol
  doi: 10.1038/msb.2013.54
– volume: 184
  start-page: 2487
  issue: 9
  year: 2021
  ident: 2022051813464665800_ref105
  article-title: Synthetic lethality-mediated precision oncology via the tumor transcriptome
  publication-title: Cell
  doi: 10.1016/j.cell.2021.03.030
– volume: 9
  start-page: 2163
  issue: 8
  year: 2013
  ident: 2022051813464665800_ref15
  article-title: Identification of synthetic lethal pairs in biological systems through network information centrality
  publication-title: Mol Biosyst
  doi: 10.1039/c3mb25589a
– volume: 355
  start-page: 1152
  issue: 6330
  year: 2017
  ident: 2022051813464665800_ref7
  article-title: PARP inhibitors: synthetic lethality in the clinic
  publication-title: Science
  doi: 10.1126/science.aam7344
– volume: 219
  start-page: e202006159
  issue: 10
  year: 2020
  ident: 2022051813464665800_ref99
  article-title: Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality
  publication-title: J Cell Biol
  doi: 10.1083/jcb.202006159
– volume: 11
  start-page: e1004506
  issue: 10
  year: 2015
  ident: 2022051813464665800_ref14
  article-title: Connectivity homology enables inter-species network models of synthetic lethality
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1004506
– volume: 28
  start-page: 246
  issue: 3
  year: 2021
  ident: 2022051813464665800_ref89
  article-title: Re-defining synthetic lethality by phenotypic profiling for precision oncology
  publication-title: Cell Chem Biol
  doi: 10.1016/j.chembiol.2021.01.026
– volume: 10
  start-page: S4
  issue: Suppl 1
  year: 2012
  ident: 2022051813464665800_ref52
  article-title: Predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using short polypeptide clusters
  publication-title: Proteome Sci
  doi: 10.1186/1477-5956-10-S1-S4
– volume: 22
  start-page: bbab036
  issue: 5
  year: 2021
  ident: 2022051813464665800_ref50
  article-title: A survey on computational models for predicting protein-protein interactions
  publication-title: Brief Bioinform
  doi: 10.1093/bib/bbab036
– volume: 2
  start-page: 77
  issue: 2
  year: 2016
  ident: 2022051813464665800_ref111
  article-title: Translation of genotype to phenotype by a hierarchy of cell subsystems
  publication-title: Cell Syst
  doi: 10.1016/j.cels.2016.02.003
– volume: 14
  start-page: 573
  issue: 6
  year: 2017
  ident: 2022051813464665800_ref44
  article-title: Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions
  publication-title: Nat Method
  doi: 10.1038/nmeth.4225
– volume: 17
  start-page: 748
  issue: 3
  year: 2020
  ident: 2022051813464665800_ref29
  article-title: SL(2)MF: predicting synthetic lethality in human cancers via logistic matrix factorization
  publication-title: IEEE/ACM Trans Comput Biol Bioinform
  doi: 10.1109/TCBB.2019.2909908
– volume: 112
  start-page: 12217
  issue: 39
  year: 2015
  ident: 2022051813464665800_ref21
  article-title: Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival
  publication-title: P Natl Acad Sci USA
  doi: 10.1073/pnas.1508573112
– volume: 6
  start-page: e1000928
  issue: 9
  year: 2010
  ident: 2022051813464665800_ref72
  article-title: An integrative multi-network and multi-classifier approach to predict genetic interactions
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1000928
– volume: 15
  start-page: 564
  issue: 9
  year: 2018
  ident: 2022051813464665800_ref3
  article-title: Synthetic lethal therapies for cancer: what's next after PARP inhibitors?
  publication-title: Nat Rev Clin Oncol
  doi: 10.1038/s41571-018-0055-6
– volume: 2020
  start-page: 449
  year: 2020
  ident: 2022051813464665800_ref79
  article-title: Predicting polypharmacy side-effects using knowledge graph embeddings
  publication-title: AMIA Jt Summits Transl Sci Proc
– volume: 49
  start-page: D92
  issue: D1
  year: 2021
  ident: 2022051813464665800_ref112
  article-title: GenBank
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkaa1023
– volume: 360
  start-page: eaao1729
  issue: 6386
  year: 2018
  ident: 2022051813464665800_ref86
  article-title: Systematic analysis of complex genetic interactions
  publication-title: Science
  doi: 10.1126/science.aao1729
– volume: 30
  start-page: 187
  issue: 1
  year: 2021
  ident: 2022051813464665800_ref35
  article-title: The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions
  publication-title: Protein Sci
  doi: 10.1002/pro.3978
– volume: 35
  start-page: 457
  issue: 3
  year: 2019
  ident: 2022051813464665800_ref63
  article-title: A platform of synthetic lethal gene interaction Networks reveals that the GNAQ uveal melanoma oncogene controls the hippo pathway through FAK
  publication-title: Cancer Cell
  doi: 10.1016/j.ccell.2019.01.009
– volume: 91
  start-page: 355
  issue: 2
  year: 2004
  ident: 2022051813464665800_ref126
  article-title: The COSMIC (catalogue of somatic mutations in cancer) database and website
  publication-title: Br J Cancer
  doi: 10.1038/sj.bjc.6601894
– volume: 120
  start-page: 405
  issue: 1
  year: 2019
  ident: 2022051813464665800_ref24
  article-title: Identification of synthetic lethality based on a functional network by using machine learning algorithms
  publication-title: J Cell Biochem
  doi: 10.1002/jcb.27395
– volume: 20
  start-page: 137
  issue: 1
  year: 2019
  ident: 2022051813464665800_ref47
  article-title: GEMINI: a variational Bayesian approach to identify genetic interactions from combinatorial CRISPR screens
  publication-title: Genome Biol
  doi: 10.1186/s13059-019-1745-9
– volume: 11
  start-page: 2375
  issue: 1
  year: 2020
  ident: 2022051813464665800_ref17
  article-title: Integration of multiple biological contexts reveals principles of synthetic lethality that affect reproducibility
  publication-title: Nat Commun
  doi: 10.1038/s41467-020-16078-y
– volume: 41
  start-page: D1021
  issue: Database issue
  year: 2013
  ident: 2022051813464665800_ref39
  article-title: GenomeRNAi: a database for cell-based and in vivo RNAi phenotypes, 2013 update
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gks1170
– volume: 10
  start-page: 427
  issue: 5
  year: 2013
  ident: 2022051813464665800_ref42
  article-title: Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping
  publication-title: Nat Methods
  doi: 10.1038/nmeth.2436
– volume: 38
  start-page: 672
  issue: 5
  year: 2020
  ident: 2022051813464665800_ref108
  article-title: Predicting drug response and Synergy using a deep learning model of human cancer cells
  publication-title: Cancer Cell
  doi: 10.1016/j.ccell.2020.09.014
– volume: 12
  start-page: 112
  issue: 1
  year: 2019
  ident: 2022051813464665800_ref16
  article-title: Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
  publication-title: BMC Med Genom
  doi: 10.1186/s12920-019-0554-z
– volume: 37
  start-page: D767
  issue: Database issue
  year: 2009
  ident: 2022051813464665800_ref122
  article-title: Human protein reference database--2009 update
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkn892
– start-page: 1
  volume-title: Icbbe 2019: 2019 6th International Conference on Biomedical and Bioinformatics Engineering
  year: 2019
  ident: 2022051813464665800_ref69
– volume: 36
  start-page: 179
  issue: 2
  year: 2018
  ident: 2022051813464665800_ref45
  article-title: Orthologous CRISPR-Cas9 enzymes for combinatorial genetic screens
  publication-title: Nat Biotechnol
  doi: 10.1038/nbt.4048
– volume: 568
  start-page: 511
  issue: 7753
  year: 2019
  ident: 2022051813464665800_ref90
  article-title: Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens
  publication-title: Nature
  doi: 10.1038/s41586-019-1103-9
– volume: 137
  start-page: 835
  issue: 5
  year: 2009
  ident: 2022051813464665800_ref133
  article-title: A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene
  publication-title: Cell
  doi: 10.1016/j.cell.2009.05.006
– volume: 174
  start-page: 953
  issue: 4
  year: 2018
  ident: 2022051813464665800_ref12
  article-title: Mapping the genetic landscape of human cells
  publication-title: Cell
  doi: 10.1016/j.cell.2018.06.010
– volume: 18
  start-page: 613
  issue: 10
  year: 2017
  ident: 2022051813464665800_ref10
  article-title: Synthetic lethality and cancer
  publication-title: Nat Rev Genet
  doi: 10.1038/nrg.2017.47
– volume: 10
  start-page: 57
  year: 2015
  ident: 2022051813464665800_ref58
  article-title: Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer
  publication-title: Biol Direct
  doi: 10.1186/s13062-015-0086-1
– volume-title: Proceedings of the Third International Conference on, Document Analysis and Recognition
  year: 1995
  ident: 2022051813464665800_ref74
– volume: 15
  start-page: 20170387
  issue: 141
  year: 2018
  ident: 2022051813464665800_ref81
  article-title: Opportunities and obstacles for deep learning in biology and medicine
  publication-title: J R Soc Interface
  doi: 10.1098/rsif.2017.0387
– volume: 22
  start-page: bbab128
  issue: 5
  year: 2021
  ident: 2022051813464665800_ref106
  article-title: Machine learning approach to gene essentiality prediction: a review
  publication-title: Brief Bioinform
  doi: 10.1093/bib/bbab128
– volume: Chapter 6
  start-page: Unit 6.12.1
  year: 2011
  ident: 2022051813464665800_ref129
  article-title: Using OrthoMCL to assign proteins to OrthoMCL-DB groups or to cluster proteomes into new ortholog groups
  publication-title: Curr Protoc Bioinformatics
– volume: 12
  start-page: 1144
  issue: 12
  year: 2021
  ident: 2022051813464665800_ref26
  article-title: Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines
  publication-title: Cell Syst
  doi: 10.1016/j.cels.2021.08.006
– volume: 4
  start-page: 671
  issue: 10
  year: 2018
  ident: 2022051813464665800_ref87
  article-title: Synthetic lethality and cancer - penetrance as the major barrier
  publication-title: Trends Cancer
  doi: 10.1016/j.trecan.2018.08.003
– volume: 11
  start-page: 9038
  issue: 18
  year: 2021
  ident: 2022051813464665800_ref64
  article-title: Mapping the landscape of synthetic lethal interactions in liver cancer
  publication-title: Theranostics
  doi: 10.7150/thno.63416
– volume: 20
  start-page: 657
  issue: Suppl 19
  year: 2019
  ident: 2022051813464665800_ref28
  article-title: Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
  publication-title: BMC Bioinform
  doi: 10.1186/s12859-019-3197-3
– volume: 3
  start-page: 116
  year: 2009
  ident: 2022051813464665800_ref53
  article-title: Human synthetic lethal inference as potential anti-cancer target gene detection
  publication-title: BMC Syst Biol
  doi: 10.1186/1752-0509-3-116
– volume: 47
  start-page: D559
  issue: D1
  year: 2019
  ident: 2022051813464665800_ref120
  article-title: CORUM: the comprehensive resource of mammalian protein complexes-2019
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gky973
– volume: 31
  start-page: 3299
  issue: 20
  year: 2015
  ident: 2022051813464665800_ref22
  article-title: Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btv352
– volume: 18
  start-page: 1028
  year: 2020
  ident: 2022051813464665800_ref82
  article-title: G2G: a web-server for the prediction of human synthetic lethal interactions
  publication-title: Comput Struct Biotechnol J
  doi: 10.1016/j.csbj.2020.04.012
– volume: 12
  start-page: 40
  issue: 1
  year: 2016
  ident: 2022051813464665800_ref96
  article-title: A synthetic lethal approach for compound and target identification in Staphylococcus aureus
  publication-title: Nat Chem Biol
  doi: 10.1038/nchembio.1967
– volume: 4
  start-page: 666
  issue: 5
  year: 2011
  ident: 2022051813464665800_ref103
  article-title: Lung-cancer chemoprevention by induction of synthetic lethality in mutant KRAS premalignant cells in vitro and in vivo
  publication-title: Cancer Prev Res (Phila)
  doi: 10.1158/1940-6207.CAPR-10-0235
– volume: 327
  start-page: 425
  issue: 5964
  year: 2010
  ident: 2022051813464665800_ref56
  article-title: The genetic landscape of a cell
  publication-title: Science
  doi: 10.1126/science.1180823
– volume: 20
  start-page: 111
  issue: 1
  year: 2021
  ident: 2022051813464665800_ref27
  article-title: Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality
  publication-title: Mol Cancer
  doi: 10.1186/s12943-021-01405-8
– volume: 696
  start-page: 191
  year: 2011
  ident: 2022051813464665800_ref68
  article-title: Decision tree and ensemble learning algorithms with their applications in bioinformatics
  publication-title: Adv Exp Med Biol
  doi: 10.1007/978-1-4419-7046-6_19
– volume: 49
  start-page: W613
  issue: W1
  year: 2021
  ident: 2022051813464665800_ref85
  article-title: SynLeGG: analysis and visualization of multiomics data for discovery of cancer 'Achilles Heels' and gene function relationships
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkab338
– volume: 16
  start-page: 321
  issue: 1
  year: 2002
  ident: 2022051813464665800_ref107
  article-title: SMOTE: synthetic minority over-sampling technique
  publication-title: J Artif Intel Res
  doi: 10.1613/jair.953
– volume: 12
  start-page: 1275
  issue: 1
  year: 2021
  ident: 2022051813464665800_ref2
  article-title: The tumor therapy landscape of synthetic lethality
  publication-title: Nat Commun
  doi: 10.1038/s41467-021-21544-2
– volume: 158
  start-page: 1199
  issue: 5
  year: 2014
  ident: 2022051813464665800_ref13
  article-title: Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality
  publication-title: Cell
  doi: 10.1016/j.cell.2014.07.027
– volume: 36
  start-page: 261
  issue: 2
  year: 2021
  ident: 2022051813464665800_ref71
  article-title: Synthetic lethal interactions prediction based on multiple similarity measures fusion
  publication-title: J Comput Sci Tech-Ch
  doi: 10.1007/s11390-021-0866-2
– volume: 3
  start-page: 119
  year: 2013
  ident: 2022051813464665800_ref54
  article-title: Proposal for a new therapy for drug-resistant malaria using plasmodium synthetic lethality inference
  publication-title: Int J Parasitol Drugs Drug Resist
  doi: 10.1016/j.ijpddr.2013.06.001
– volume: 19
  start-page: 644
  issue: 4
  year: 2018
  ident: 2022051813464665800_ref88
  article-title: A landscape of synthetic viable interactions in cancer
  publication-title: Brief Bioinform
– volume: 62
  start-page: e01533
  issue: 4
  year: 2018
  ident: 2022051813464665800_ref98
  article-title: Using yeast synthetic lethality to inform drug combination for malaria
  publication-title: Antimicrob Agents Chemother
  doi: 10.1128/AAC.01533-17
– volume: 2
  start-page: 56
  issue: 1
  year: 2020
  ident: 2022051813464665800_ref76
  article-title: From local explanations to global understanding with explainable AI for trees
  publication-title: Nat Mach Intell
  doi: 10.1038/s42256-019-0138-9
– volume: 5
  start-page: 241
  issue: 1
  year: 2020
  ident: 2022051813464665800_ref9
  article-title: Development of synthetic lethality in cancer: molecular and cellular classification
  publication-title: Signal Transduct Target Ther
  doi: 10.1038/s41392-020-00358-6
– volume: 1
  start-page: 417
  issue: 6
  year: 2015
  ident: 2022051813464665800_ref116
  article-title: The molecular signatures database (MSigDB) hallmark gene set collection
  publication-title: Cell Syst
  doi: 10.1016/j.cels.2015.12.004
– volume: 44
  start-page: D1011
  issue: D1
  year: 2016
  ident: 2022051813464665800_ref34
  article-title: SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkv1108
– volume-title: International Conference on Learning Representations
  ident: 2022051813464665800_ref78
– volume: 8
  start-page: 15580
  year: 2017
  ident: 2022051813464665800_ref65
  article-title: Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data
  publication-title: Nat Commun
  doi: 10.1038/ncomms15580
– volume: 7
  start-page: 55352
  issue: 34
  year: 2016
  ident: 2022051813464665800_ref131
  article-title: Ranking novel cancer driving synthetic lethal gene pairs using TCGA data
  publication-title: Oncotarget
  doi: 10.18632/oncotarget.10536
– volume: 25
  start-page: 4041
  issue: 10
  year: 2021
  ident: 2022051813464665800_ref33
  article-title: Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
  publication-title: IEEE J Biomed Health Inform
  doi: 10.1109/JBHI.2021.3079302
– volume: 69
  start-page: 699
  issue: 4
  year: 2018
  ident: 2022051813464665800_ref46
  article-title: Combinatorial CRISPR-Cas9 metabolic screens reveal critical redox control points dependent on the KEAP1-NRF2 regulatory Axis
  publication-title: Mol Cell
  doi: 10.1016/j.molcel.2018.01.017
– volume: 9
  start-page: 426
  year: 2008
  ident: 2022051813464665800_ref67
  article-title: Mining protein networks for synthetic genetic interactions
  publication-title: BMC Bioinform
  doi: 10.1186/1471-2105-9-426
– volume: 47
  start-page: D948
  issue: D1
  year: 2019
  ident: 2022051813464665800_ref117
  article-title: The comparative Toxicogenomics database: update 2019
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gky868
– volume: 8
  start-page: 8440
  issue: 1
  year: 2018
  ident: 2022051813464665800_ref18
  article-title: Synthetic lethality-based identification of targets for anticancer drugs in the human Signaling network
  publication-title: Sci Rep
  doi: 10.1038/s41598-018-26783-w
– volume: 49
  start-page: D480
  issue: D1
  year: 2021
  ident: 2022051813464665800_ref113
  article-title: UniProt: the universal protein knowledgebase in 2021
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkaa1100
– volume: 37
  start-page: i418
  issue: Suppl_1
  year: 2021
  ident: 2022051813464665800_ref80
  article-title: KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btab271
– volume: 9
  start-page: 2546
  issue: 1
  year: 2018
  ident: 2022051813464665800_ref60
  article-title: Harnessing synthetic lethality to predict the response to cancer treatment
  publication-title: Nat Commun
  doi: 10.1038/s41467-018-04647-1
– volume: 45
  start-page: D353
  issue: D1
  year: 2017
  ident: 2022051813464665800_ref115
  article-title: KEGG: new perspectives on genomes, pathways, diseases and drugs
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gkw1092
– volume: 353
  start-page: aaf1420
  issue: 6306
  year: 2016
  ident: 2022051813464665800_ref40
  article-title: A global genetic interaction network maps a wiring diagram of cellular function
  publication-title: Science
  doi: 10.1126/science.aaf1420
– volume: 5
  start-page: 11
  issue: 1
  year: 2019
  ident: 2022051813464665800_ref11
  article-title: A road map to personalizing targeted cancer therapies using synthetic lethality
  publication-title: Trends Cancer
  doi: 10.1016/j.trecan.2018.11.001
– volume: 19
  start-page: 23
  issue: 1
  year: 2020
  ident: 2022051813464665800_ref1
  article-title: Synthetic lethality as an engine for cancer drug target discovery
  publication-title: Nat Rev Drug Discov
  doi: 10.1038/s41573-019-0046-z
– volume: 36
  start-page: 2209
  issue: 7
  year: 2020
  ident: 2022051813464665800_ref30
  article-title: Predicting synthetic lethal interactions using heterogeneous data sources
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btz893
– volume: 73
  start-page: 6128
  issue: 20
  year: 2013
  ident: 2022051813464665800_ref55
  article-title: A comparative genomic approach for identifying synthetic lethal interactions in human cancer
  publication-title: Cancer Res
  doi: 10.1158/0008-5472.CAN-12-3956
– volume: 34
  start-page: D535
  issue: Database issue
  year: 2006
  ident: 2022051813464665800_ref37
  article-title: BioGRID: a general repository for interaction datasets
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkj109
– volume: 434
  start-page: 917
  issue: 7035
  year: 2005
  ident: 2022051813464665800_ref6
  article-title: Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy
  publication-title: Nature
  doi: 10.1038/nature03445
– volume: 46
  start-page: 175
  issue: 3
  year: 1992
  ident: 2022051813464665800_ref70
  article-title: An introduction to kernel and nearest-neighbor nonparametric regression
  publication-title: Am Stat
  doi: 10.1080/00031305.1992.10475879
– volume: 6
  start-page: 30
  year: 2013
  ident: 2022051813464665800_ref61
  article-title: Identification of potential synthetic lethal genes to p53 using a computational biology approach
  publication-title: BMC Med Genom
  doi: 10.1186/1755-8794-6-30
– volume: 37
  year: 2021
  ident: 2022051813464665800_ref32
  article-title: Graph contextualized attention network for predicting synthetic lethality in human cancers
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btab110
– volume: 28
  start-page: 938
  issue: 4
  year: 2019
  ident: 2022051813464665800_ref84
  article-title: Beyond synthetic lethality: charting the landscape of pairwise gene expression states associated with survival in cancer
  publication-title: Cell Rep
  doi: 10.1016/j.celrep.2019.06.067
– volume: 13
  start-page: 118
  issue: 1
  year: 2020
  ident: 2022051813464665800_ref8
  article-title: Advances in synthetic lethality for cancer therapy: cellular mechanism and clinical translation
  publication-title: J Hematol Oncol
  doi: 10.1186/s13045-020-00956-5
– volume: 13
  start-page: 1541002
  issue: 3
  year: 2015
  ident: 2022051813464665800_ref19
  article-title: Predicting essential genes and synthetic lethality via influence propagation in signaling pathways of cancer cell fates
  publication-title: J Bioinform Comput Biol
  doi: 10.1142/S0219720015410024
– volume: 36
  start-page: 4458
  issue: 16
  year: 2020
  ident: 2022051813464665800_ref31
  article-title: Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btaa211
– volume: 47
  start-page: D330
  issue: D1
  year: 2019
  ident: 2022051813464665800_ref114
  article-title: The gene ontology resource: 20 years and still GOing strong
  publication-title: Nucleic Acid Res
  doi: 10.1093/nar/gky1055
– volume: 16
  start-page: 487
  issue: 3
  year: 2004
  ident: 2022051813464665800_ref135
  article-title: A robust toolkit for functional profiling of the yeast genome
  publication-title: Mol Cell
  doi: 10.1016/j.molcel.2004.09.035
– volume: 63
  start-page: 514
  issue: 3
  year: 2016
  ident: 2022051813464665800_ref49
  article-title: A network of conserved synthetic lethal interactions for exploration of precision cancer therapy
  publication-title: Mol Cell
  doi: 10.1016/j.molcel.2016.06.022
– volume: 15
  start-page: 1929
  issue: 1
  year: 2014
  ident: 2022051813464665800_ref77
  article-title: Dropout: a simple way to prevent neural Networks from overfitting
  publication-title: J Mach Learn Res
– volume: 20
  start-page: 1295
  issue: 4
  year: 2019
  ident: 2022051813464665800_ref59
  article-title: Link synthetic lethality to drug sensitivity of cancer cells
  publication-title: Brief Bioinform
  doi: 10.1093/bib/bbx172
– volume: 10
  start-page: 3947
  issue: 1
  year: 2020
  ident: 2022051813464665800_ref66
  article-title: Deep learning applied to hyperspectral endoscopy for online spectral classification
  publication-title: Sci Rep
  doi: 10.1038/s41598-020-60574-6
– volume: 27
  start-page: 1226
  issue: 8
  year: 2005
  ident: 2022051813464665800_ref75
  article-title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
  publication-title: IEEE Trans Pattern Anal Mach Intell
  doi: 10.1109/TPAMI.2005.159
– volume: 7
  start-page: eabg2106
  issue: 31
  year: 2021
  ident: 2022051813464665800_ref92
  article-title: Synthetic dysmobility screen unveils an integrated STK40-YAP-MAPK system driving cell migration
  publication-title: Sci Adv
  doi: 10.1126/sciadv.abg2106
– volume: 43
  start-page: 134
  issue: 1
  year: 2016
  ident: 2022051813464665800_ref104
  article-title: Hereditary cancer syndromes as model systems for chemopreventive agent development
  publication-title: Semin Oncol
  doi: 10.1053/j.seminoncol.2015.09.015
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Snippet Abstract Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of...
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes...
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SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
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SubjectTerms Cancer
Cell death
Cell survival
Computer applications
Computer networks
Deactivation
Deep learning
Genes
Inactivation
Lethality
Machine learning
Poly(ADP-ribose) polymerase
Predictions
Review
Sampling methods
Software
Statistical methods
Title Computational methods, databases and tools for synthetic lethality prediction
URI https://www.ncbi.nlm.nih.gov/pubmed/35352098
https://www.proquest.com/docview/2675447587
https://www.proquest.com/docview/2645471564
https://pubmed.ncbi.nlm.nih.gov/PMC9116379
Volume 23
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