https://m2.mtmt.hu/ hun 2026-09-22 02:45 JournalArticle 32690009 VALIDATED true Funding Agency and Grant Number: Semmelweis University; National Research, Development and Innovation Office of Hungary (NKFIA) [NVKP-16-1-2016-0017, K131458]; Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary of the Semmelweis University [2020-4.1.1.-TKP2020, TKP2021-EGA-24]; Research Excellence Programme of the National Research, Development and Innovation Office of the Ministry of Innovation and Technology in Hungary (TKP/ITM/NKFIH); New National Excellence Program of the Ministry for Innovation and Technology from the National Research, Development and Innovation Fund [UNKP-20-4-I-SE-7, UNKP-21-4-II-SE-18]; "Semmelweis 250+ Kivalosagi PhD Osztondij" grant [EFOP3.6.3-VEKOP-16-2017-00009] Funding text: Open access funding provided by Semmelweis University. This study was supported by the National Research, Development and Innovation Office of Hungary (NKFIA; NVKP-16-1-2016-0017 National Heart Program; K131458), Thematic Excellence Programme (2020-4.1.1.-TKP2020, TKP2021-EGA-24) of the Ministry for Innovation and Technology in Hungary, within the framework of the Therapeutic Development and Molecular Biology thematic programmes of the Semmelweis University. This study was also supported by the Research Excellence Programme of the National Research, Development and Innovation Office of the Ministry of Innovation and Technology in Hungary (TKP/ITM/NKFIH)". B.A. was supported by the UNKP-20-4-I-SE-7 and the UNKP-21-4-II-SE-18 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund. MP was supported by EFOP3.6.3-VEKOP-16-2017-00009 "Semmelweis 250+ Kivalosagi PhD Osztondij" grant. The funding bodies did not play any role in the design of the study, in the collection, analysis and interpretation of data or in writing of the manuscript. 0 false 2026-09-06T16:10:33.750+0000 2025-03-06T22:01:27.950+0000 2022-02-20T14:12:27.629+0000 true 1035707 Csermely Péter /api/author/1035707 Author Csermely Péter (Biokémia) true 1035707 2025-07-07T12:55:39.687+0000 2025-07-07T12:55:39.687+0000 2024-09-10T07:06:35.568+0000 true 10015544 Csajbók Edit /api/admin/10015544 Admin Csajbók Edit (SE_KK_Admin5_CSE, admin) true 10015544 true false true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType Szakcikk (Folyóiratcikk) 101 true 10000059 true 1 /api/category/1 Category Tudományos true 1 Balogh, Olivér M. true true /api/journal/10001136 REVIEWED BMC BIOINFORMATICS 1471-2105 1471-2105 true false 10001136 false 10001136 true 1471-2105 1471-2105 Journal FOREIGN 23 1 78 78 19 2022 Funding 2011279 /api/funding/2011279 (Open access funding of Semmelweis University) false true Funding 2011280 /api/funding/2011280 (NVKP-16-1-2016-0017) false true Funding 2011281 /api/funding/2011281 (K131458) false true Funding 2011282 /api/funding/2011282 Thematic Excellence Program (Semmelweis University)(2020-4.1.1.-TKP2020) Támogató: Innovációs és Technológiai Minisztérium false true Funding 2011283 /api/funding/2011283 Thematic Institutional Excellence Programme(TKP2021-EGA-24) Támogató: Emberi Erőforrások Minisztériuma false true Funding 2011284 /api/funding/2011284 (ÚNKP-20-4-I-SE-7) false true Funding 2011285 /api/funding/2011285 (ÚNKP-21-4-II-SE-18) false true Funding 2011286 /api/funding/2011286 Az orvos-, egészségtudományi- és gyógyszerészképzés tudományos műhelyeinek fejlesztése(EFOP-3.6.3-VEKOP-16-2017-00009) Támogató: EFOP-VEKOP false true Funding 2011287 /api/funding/2011287 (Semmelweis 250+ Kiválósági PhD Ösztöndíj) false true true true 2022 true true true false true false GOLD 2026-09-06 true https://doi.org/10.1186/s12859-022-04598-x 42 0 37 35 2 36 37 30 35 39 37 37 35 40 2 0 42 40 37 35 0 1 1 Folyóiratcikk Journal Article 24 28 Könyvrészlet Chapter in Book 25 7 Könyv Book 23 2 Egyéb konferenciaközlemény Conference paper 31 0 Egyéb konferenciakötet Conference proceedings 32 0 Oltalmi formák Protection forms 26 0 Disszertáció Thesis 28 3 Egyéb Miscellaneous 29 2 Alkotás Achievement 22 0 Kutatási adat Research data 33 0 Folyóiratcikk Journal Article 24 0 0 0 Könyvrészlet Chapter in Book 25 0 0 0 Könyv Book 23 0 0 0 Egyéb konferenciaközlemény Conference paper 31 0 0 0 Egyéb konferenciakötet Conference proceedings 32 0 0 0 Oltalmi formák Protection forms 26 0 0 0 Disszertáció Thesis 28 0 0 0 Egyéb Miscellaneous 29 0 0 0 Alkotás Achievement 22 0 0 0 Kutatási adat Research data 33 0 0 0 2022 0 6 6 6 6 2023 0 10 9 10 9 2024 0 9 9 9 9 2025 0 12 11 12 11 2026 0 5 5 5 5 Q1 false 10082699,1035707,10069234 false false false 2022-03-01T11:02:44.330+0000 Farmakológiai és Farmakoterápiás Intézet (SE / AOK / I); MTA-SE Rendszerfarmakológiai Kutatócsoport (SE / AOK / I / FFI); Molekuláris Biológiai Tanszék (SE / AOK / I / BMBI) 7 30 3 7 0 true Language 10002 /api/language/10002 Angol Angol English true 2 true PersonAuthorship 100735750 /api/authorship/100735750 Balogh, Olivér M. [Balogh, Olivér Márton (Mesterséges intel...), szerző] Farmakológiai és Farmakoterápiás Intézet (SE / AOK / I); MTA-SE Rendszerfarmakológiai Kutatócsoport (SE / AOK / I / FFI) 1 0.143 true false false Author 10082699 /api/author/10082699 Balogh Olivér Márton (Mesterséges intelligencia, Adat tudomány) Balogh Olivér Márton true true Balogh Olivér M. true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 100735751 /api/authorship/100735751 Benczik, Bettina [Benczik, Bettina (Bioinformatika), szerző] Farmakológiai és Farmakoterápiás Intézet (SE / AOK / I); MTA-SE Rendszerfarmakológiai Kutatócsoport (SE / AOK / I / FFI) 2 0.14285715 false false false Author 10069234 /api/author/10069234 Benczik Bettina (Bioinformatika) Benczik Bettina true true Benczik Bettina true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 100735752 /api/authorship/100735752 Horváth, András [Horváth, András (mérnöki tudományok), szerző] 3 0.14285715 false false false Author 10029872 /api/author/10029872 Horváth András (mérnöki tudományok) Horváth András true 10029872 true Horváth András true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 100735753 /api/authorship/100735753 Pétervári, Mátyás [Pétervári, Mátyás (Farmakológia), szerző] Farmakológiai és Farmakoterápiás Intézet (SE / AOK / I); MTA-SE Rendszerfarmakológiai Kutatócsoport (SE / AOK / I / FFI) 4 0.14285715 false false false Author 10065329 /api/author/10065329 Pétervári Mátyás (Farmakológia) Pétervári Mátyás true 10065329 true Pétervári Mátyás true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 100735754 /api/authorship/100735754 Csermely, Péter [Csermely, Péter (Biokémia), szerző] Molekuláris Biológiai Tanszék (SE / AOK / I / BMBI) 5 0.143 false false false Author 1035707 /api/author/1035707 Csermely Péter (Biokémia) Csermely Péter true 1035707 true Csermely Péter true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 100735755 /api/authorship/100735755 Ferdinandy, Péter [Ferdinandy, Péter (Farmakológia, mol...), szerző] Farmakológiai és Farmakoterápiás Intézet (SE / AOK / I); MTA-SE Rendszerfarmakológiai Kutatócsoport (SE / AOK / I / FFI) 6 0.143 false false false Author 10010125 /api/author/10010125 Ferdinandy Péter (Farmakológia, molekuláris medicina) Ferdinandy Péter true 10010125 true Ferdinandy Péter true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 100735756 /api/authorship/100735756 Ágg, Bence ✉ [Ágg, Bence (Farmakológia), szerző] Farmakológiai és Farmakoterápiás Intézet (SE / AOK / I); MTA-SE Rendszerfarmakológiai Kutatócsoport (SE / AOK / I / FFI) 7 0.143 false true true Author 10057401 /api/author/10057401 Ágg Bence (Farmakológia) Ágg Bence true 10057401 true Ágg Bence true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PublicationIdentifier 20649260 /api/publicationidentifier/20649260 DOI: 10.1186/s12859-022-04598-x PlainSource 6 /api/publicationsource/6 DOI PublicationSourceType 10001 /api/publicationsourcetype/10001 DOI true true true DOI DOI https://doi.org/@@@ true true 6 true GOLD true IDENTICAL 10.1186/s12859-022-04598-x https://doi.org/10.1186/s12859-022-04598-x false true PublicationIdentifier 20770147 /api/publicationidentifier/20770147 WoS: 000757934300002 PlainSource 1 /api/publicationsource/1 WoS PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true IDENTICAL 000757934300002 https://www.webofscience.com/wos/woscc/full-record/000757934300002 true true PublicationIdentifier 20715339 /api/publicationidentifier/20715339 Scopus: 85125020446 PlainSource 3 /api/publicationsource/3 Scopus PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true Scopus Scopus http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-@@@ true true 3 true IDENTICAL 85125020446 http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85125020446 false true PublicationIdentifier 20662736 /api/publicationidentifier/20662736 PubMed: 35183129 PlainSource 17 /api/publicationsource/17 PubMed PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true PubMed PubMed http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=@@@&dopt=Abstract true true 17 true IDENTICAL 35183129 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=35183129&dopt=Abstract true true Classification 12197 /api/classification/12197 Gyógyszerészet, farmakogenomika, gyógyszerkutatás és tervezés, gyógyszeres kezelés true true SjrRating 11307274 /api/sjrrating/11307274 sjr:Q1 (2022) Scopus - Applied Mathematics BMC BIOINFORMATICS 1471-2105 1471-2105 90 0.24 journal RatingType 10002 /api/ratingtype/10002 sjr sjr true true ClassificationExternal 2604 /api/classificationexternal/2604 Scopus - Applied Mathematics true 2604 true Q1 DIRECT true true Reference 28386457 /api/reference/28386457 1. Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56–68. https://doi.org/10.1038/nrg2918., DOI: 10.1038/nrg2918 1 10.1038/nrg2918 false true Reference 28386458 /api/reference/28386458 2. Wang RS, Loscalzo J. Network-based disease module discovery by a novel seed connector algorithm with pathobiological implications. J Mol Biol. 2018;430:2939–50. https://doi.org/10.1016/j.jmb.2018.05.016., DOI: 10.1016/j.jmb.2018.05.016 2 10.1016/j.jmb.2018.05.016 false true Reference 28386459 /api/reference/28386459 3. Langhauser F, Casas AI, Dao VTV, Guney E, Menche J, Geuss E, et al. A diseasome cluster-based drug repurposing of soluble guanylate cyclase activators from smooth muscle relaxation to direct neuroprotection. NPJ Syst Biol Appl. 2018;4:8. https://doi.org/10.1038/s41540-017-0039-7., DOI: 10.1038/s41540-017-0039-7 3 10.1038/s41540-017-0039-7 false true Reference 28386460 /api/reference/28386460 4. Brückner A, Polge C, Lentze N, Auerbach D, Schlattner U. Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci. 2009;10:2763–88. https://doi.org/10.3390/ijms10062763., DOI: 10.3390/ijms10062763 4 10.3390/ijms10062763 false true Reference 28386461 /api/reference/28386461 5. Völkel P, Le Faou P, Angrand PO. Interaction proteomics: Characterization of protein complexes using tandem affinity purification-mass spectrometry. Biochem Soc Trans. 2010;38:883–7. https://doi.org/10.1042/BST0380883., DOI: 10.1042/BST0380883 5 10.1042/BST0380883 false true Reference 28386462 /api/reference/28386462 6. Kaushansky A, Allen JE, Gordus A, Stiffler MA, Karp ES, Chang BH, et al. Quantifying protein-protein interactions in high throughput using protein domain microarrays. Nat Protoc. 2010;5:773–90. https://doi.org/10.1038/nprot.2010.36., DOI: 10.1038/nprot.2010.36 6 10.1038/nprot.2010.36 false true Reference 28386463 /api/reference/28386463 7. Zahiri J, Bozorgmehr J, Masoudi-Nejad A. Computational prediction of protein–protein interaction networks: algorithms and resources. Curr Genomics. 2013;14:397–414. https://doi.org/10.2174/1389202911314060004., DOI: 10.2174/1389202911314060004 7 10.2174/1389202911314060004 false true Reference 28386464 /api/reference/28386464 8. Yu W, Jo S, Lakkaraju SK, Weber DJ, MacKerell AD. Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology. Proteins Struct Funct Bioinform. 2019;87:289–301. https://doi.org/10.1002/prot.25650., DOI: 10.1002/prot.25650 8 10.1002/prot.25650 false true Reference 28386465 /api/reference/28386465 9. Das S, Chakrabarti S. Classification and prediction of protein–protein interaction interface using machine learning algorithm. Sci Rep. 2021;11:1761. https://doi.org/10.1038/s41598-020-80900-2., DOI: 10.1038/s41598-020-80900-2 9 10.1038/s41598-020-80900-2 false true Reference 28386466 /api/reference/28386466 10. Maetschke SR, Simonsen M, Davis MJ, Ragan MA. Gene ontology-driven inference of protein-protein interactions using inducers. Bioinformatics. 2012;28:69–75. https://doi.org/10.1093/bioinformatics/btr610., DOI: 10.1093/bioinformatics/btr610 10 10.1093/bioinformatics/btr610 false true Reference 28386467 /api/reference/28386467 11. Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K, et al. Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci U S A. 2007;104:4337–41. https://doi.org/10.1073/pnas.0607879104., DOI: 10.1073/pnas.0607879104 11 10.1073/pnas.0607879104 false true Reference 28386468 /api/reference/28386468 12. Guo Y, Yu L, Wen Z, Li M. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucl Acids Res. 2008;36:3025–30. https://doi.org/10.1093/nar/gkn159., DOI: 10.1093/nar/gkn159 12 10.1093/nar/gkn159 false true Reference 28386469 /api/reference/28386469 13. Huang YA, You ZH, Gao X, Wong L, Wang L. Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence. Biomed Res Int. 2015;2015:902198. https://doi.org/10.1155/2015/902198., DOI: 10.1155/2015/902198 13 10.1155/2015/902198 false true Reference 28386470 /api/reference/28386470 14. Wang YB, You ZH, Li X, Jiang TH, Chen X, Zhou X, et al. Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Mol Biosyst. 2017;13:1336–44. https://doi.org/10.1039/c7mb00188f., DOI: 10.1039/c7mb00188f 14 10.1039/c7mb00188f false true Reference 28386471 /api/reference/28386471 15. Du X, Sun S, Hu C, Yao Y, Yan Y, Zhang Y. DeepPPI: boosting prediction of protein-protein interactions with deep neural networks. J Chem Inf Model. 2017;57:1499–510. https://doi.org/10.1021/acs.jcim.7b00028., DOI: 10.1021/acs.jcim.7b00028 15 10.1021/acs.jcim.7b00028 false true Reference 28386472 /api/reference/28386472 16. Gui Y, Wang R, Wei Y, Wang X. DNN-PPI: a large-scale prediction of protein-protein interactions based on deep neural networks. J Biol Syst. 2019;27:1–18. https://doi.org/10.1142/S0218339019500013., DOI: 10.1142/S0218339019500013 16 10.1142/S0218339019500013 false true Reference 28386473 /api/reference/28386473 17. Li H, Gong XJ, Yu H, Zhou C. Deep neural network based predictions of protein interactions using primary sequences. Molecules. 2018;23:1923. https://doi.org/10.3390/molecules23081923., DOI: 10.3390/molecules23081923 17 10.3390/molecules23081923 false true Reference 28386474 /api/reference/28386474 18. Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. In: 12th International conference on information and knowledge management (CIKM). 2004. p. 556–559. https://doi.org/10.1145/956863.956972., DOI: 10.1145/956863.956972 18 10.1145/956863.956972 false true Reference 28386475 /api/reference/28386475 19. Linyuan LL, Zhou T. Link prediction in complex networks: a survey. Phys A Stat Mech Appl. 2011;390:1150–70. https://doi.org/10.1016/j.physa.2010.11.027., DOI: 10.1016/j.physa.2010.11.027 19 10.1016/j.physa.2010.11.027 false true Reference 28386476 /api/reference/28386476 20. Haghani S, Keyvanpour MR. A systemic analysis of link prediction in social network. Artif Intell Rev. 2017;52:1961–95. https://doi.org/10.1007/s10462-017-9590-2., DOI: 10.1007/s10462-017-9590-2 20 10.1007/s10462-017-9590-2 false true Reference 28386477 /api/reference/28386477 21. Al Hasan M, Chaoji V, Salem S, Zaki M. Link prediction using supervised learning. In: Fourth workshop on link analysis, counterterrorism and security, Bethesda, Maryland, USA; 2006. http://www.cs.rpi.edu/~zaki/PaperDir/LINK06.pdf%5Cnpapers3://publication/uuid/1FFB1917-8A37-4698-BC04-42D04DD7AB2B. Accessed 6 May 2021. 21 false true Reference 28386478 /api/reference/28386478 22. Goyal P, Ferrara E. Graph embedding techniques, applications, and performance: a survey. Knowl Based Syst. 2017;151:78–94. https://doi.org/10.1016/j.knosys.2018.03.022., DOI: 10.1016/j.knosys.2018.03.022 22 10.1016/j.knosys.2018.03.022 false true Reference 28386479 /api/reference/28386479 23. Hamilton WL, Ying R, Leskovec J. Representation Learning on Graphs: Methods and Applications. arXiv. 2017. http://arxiv.org/abs/1709.05584. Accessed 6 May 2021. 23 false true Reference 28386480 /api/reference/28386480 24. Li T, Wang B, Jiang Y, Zhang Y, Yan Y. Restricted Boltzmann machine-based approaches for link prediction in dynamic networks. IEEE Access. 2018;6:29940–51. https://doi.org/10.1109/ACCESS.2018.2840054., DOI: 10.1109/ACCESS.2018.2840054 24 10.1109/ACCESS.2018.2840054 false true Reference 28386481 /api/reference/28386481 25. Liu F, Liu B, Sun C, Liu M, Wang X. Deep belief network-based approaches for link prediction in signed social networks. Entropy. 2015;17:2140–69. https://doi.org/10.3390/e17042140., DOI: 10.3390/e17042140 25 10.3390/e17042140 false true Reference 28386482 /api/reference/28386482 26. Su C, Tong J, Zhu Y, Cui P, Wang F. Network embedding in biomedical data science. Brief Bioinform. 2018;21:182–97. https://doi.org/10.1093/bib/bby117., DOI: 10.1093/bib/bby117 26 10.1093/bib/bby117 false true Reference 28386483 /api/reference/28386483 27. Yue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, et al. Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics. 2020;36:1241–51. https://doi.org/10.1093/bioinformatics/btz718., DOI: 10.1093/bioinformatics/btz718 27 10.1093/bioinformatics/btz718 false true Reference 28386484 /api/reference/28386484 28. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. 2014. http://www.github.com/goodfeli/adversarial. Accessed 6 May 2021. 28 false true Reference 28386485 /api/reference/28386485 29. Gao M, Zhang J, Yu J, Li J, Wen J, Xiong Q. Recommender systems based on generative adversarial networks: a problem-driven perspective. Inf Sci (NY). 2020;546:1166–85., DOI: 10.1016/j.ins.2020.09.013 29 10.1016/j.ins.2020.09.013 false true Reference 28386486 /api/reference/28386486 30. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial autoencoders. 2015. http://arxiv.org/abs/1511.05644. Accessed 6 May 2021. 30 false true Reference 28386487 /api/reference/28386487 31. Dai Q, Li Q, Tang J, Wang D. Adversarial network embedding. In: 32nd AAAI conference of artificial intelligence AAAI 2018. 2017; p. 2167–74. http://arxiv.org/abs/1711.07838. Accessed 6 May 2021. 31 false true Reference 28386488 /api/reference/28386488 32. Lei K, Qin M, Bai B, Zhang G, Yang M. GCN-GAN: a non-linear temporal link prediction model for weighted dynamic networks. In: Proceedings of IEEE INFOCOM. 2019; p. 388–96. http://arxiv.org/abs/1901.09165. Accessed 6 May 2021. 32 false true Reference 28386489 /api/reference/28386489 33. Chen J, Lin X, Jia C, Li Y, Wu Y, Zheng H, et al. Generative dynamic link prediction. Chaos Interdiscip J Nonlinear Sci. 2019;29(12):123111. https://doi.org/10.1063/1.5120722., DOI: 10.1063/1.5120722 33 10.1063/1.5120722 false true Reference 28386490 /api/reference/28386490 34. Mirza M, Osindero S. Conditional generative adversarial nets. 2014. http://arxiv.org/abs/1411.1784. Accessed 6 May 2021. 34 false true Reference 28386491 /api/reference/28386491 35. Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings 30th IEEE conference on computer vision and pattern recognition, CVPR 2017. Institute of Electrical and Electronics Engineers Inc.; 2017. p. 5967–76. http://arxiv.org/abs/1611.07004. Accessed 6 May 2021., DOI: 10.1109/CVPR.2017.632 35 10.1109/CVPR.2017.632 false true Reference 28386492 /api/reference/28386492 36. Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, et al. Scikit-learn: machine learning in Python. In: ECML PKDD workshop: languages for data mining and machine learning. 2013. p. 108–22. 36 false true Reference 28386493 /api/reference/28386493 37. Lee CY. An algorithm for path connections and its applications. IRE Trans Electron Comput. 1961;EC10:346–65. https://doi.org/10.1109/TEC.1961.5219222., DOI: 10.1109/TEC.1961.5219222 37 10.1109/TEC.1961.5219222 false true Reference 28386494 /api/reference/28386494 38. Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. arXiv. 2017. http://arxiv.org/abs/1701.07875. Accessed 6 May 2021. 38 false true Reference 28386495 /api/reference/28386495 39. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A. Improved training of wasserstein GANs. Adv Neural Inf Process Syst. 2017; p. 5768–78. http://arxiv.org/abs/1704.00028. Accessed 6 May 2021. 39 false true Reference 28386496 /api/reference/28386496 40. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. 2015. www.tensorflow.org. Accessed 20 May 2021. 40 false true Reference 28386497 /api/reference/28386497 41. Chollet F, et al. Keras. 2015. 41 false true Reference 28386498 /api/reference/28386498 42. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8: a global view on proteins and their functional interactions in 630 organisms. Nucl Acids Res. 2009;37(Suppl. 1):D412-6. https://doi.org/10.1093/nar/gkn760., DOI: 10.1093/nar/gkn760 42 10.1093/nar/gkn760 false true Reference 28386499 /api/reference/28386499 43. Grover A, Leskovec J. node2vec: scalable feature learning for networks. Proc ACM SIGKDD Int Conf Knowl Discov Data Min. 2016; p. 855–64. http://arxiv.org/abs/1607.00653. Accessed 6 May 2021., DOI: 10.1145/2939672.2939754 43 10.1145/2939672.2939754 false true Reference 28386500 /api/reference/28386500 44. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9. https://doi.org/10.1038/75556., DOI: 10.1038/75556 44 10.1038/75556 false true Reference 28386501 /api/reference/28386501 45. Carbon S, Douglass E, Good BM, Unni DR, Harris NL, Mungall CJ, et al. The gene ontology resource: enriching a GOld mine. Nucl Acids Res. 2021;49:D325–34. https://doi.org/10.1093/NAR/GKAA1113., DOI: 10.1093/NAR/GKAA1113 45 10.1093/NAR/GKAA1113 false true Reference 28386502 /api/reference/28386502 46. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498. https://doi.org/10.1101/GR.1239303., DOI: 10.1101/GR.1239303 46 10.1101/GR.1239303 false true Reference 28386503 /api/reference/28386503 47. Ágg B, Császár A, Szalay-Bekő M, Veres DV, Mizsei R, Ferdinandy P, et al. The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein–protein interaction and signalling networks. Bioinformatics. 2019;35:4490-2. https://doi.org/10.1093/BIOINFORMATICS/BTZ257., DOI: 10.1093/BIOINFORMATICS/BTZ257 47 10.1093/BIOINFORMATICS/BTZ257 false true Reference 28386504 /api/reference/28386504 48. Blohm P, Frishman G, Smialowski P, Goebels F, Wachinger B, Ruepp A, et al. Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucl Acids Res. 2014;42:D396-400. https://doi.org/10.1093/nar/gkt1079., DOI: 10.1093/nar/gkt1079 48 10.1093/nar/gkt1079 false true Reference 28386505 /api/reference/28386505 49. Ben-Hur A, Noble WS. Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinform. 2006;7(SUPPL. 1):S2. https://doi.org/10.1186/1471-2105-7-S1-S2., DOI: 10.1186/1471-2105-7-S1-S2 49 10.1186/1471-2105-7-S1-S2 false true Reference 28386506 /api/reference/28386506 50. Hashemifar S, Neyshabur B, Khan AA, Xu J. Predicting protein–protein interactions through sequence-based deep learning. Bioinformatics. 2018;34:i802–10. https://doi.org/10.1093/BIOINFORMATICS/BTY573., DOI: 10.1093/BIOINFORMATICS/BTY573 50 10.1093/BIOINFORMATICS/BTY573 false true Reference 28386507 /api/reference/28386507 51. Lopes I, Altab G, Raina P, de Magalhães JP. Gene size matters: an analysis of gene length in the human genome. Front Genet. 2021. https://doi.org/10.3389/FGENE.2021.559998., DOI: 10.3389/FGENE.2021.559998 51 10.3389/FGENE.2021.559998 false true /api/publication/32690009 Balogh Olivér M. et al. Efficient link prediction in the protein–protein interaction network using topological information in a generative adversarial network machine learning model. (2022) BMC BIOINFORMATICS 1471-2105 1471-2105 23 1<div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Forrásközlemény" style="float: left" src="/frontend/resources/grid/publication-core-icon.png"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" mtid="10082699"><a href="/gui2/?type=authors&mode=browse&sel=10082699" target="_blank">Balogh Olivér M. (<span class="authorship-author-name">Balogh Olivér Márton</span> <span class="authorAux-mtmt"> Mesterséges intelligencia, Adat tudomány</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/Farmakológiai és Farmakoterápiás Intézet; <span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/<span title="Farmakológiai és Farmakoterápiás Intézet">FFI</span>/MTA-SE Rendszerfarmakológiai Kutatócsoport</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10069234"><a href="/gui2/?type=authors&mode=browse&sel=10069234" target="_blank">Benczik Bettina (<span class="authorship-author-name">Benczik Bettina</span> <span class="authorAux-mtmt"> Bioinformatika</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/Farmakológiai és Farmakoterápiás Intézet; <span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/<span title="Farmakológiai és Farmakoterápiás Intézet">FFI</span>/MTA-SE Rendszerfarmakológiai Kutatócsoport</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10029872"><a href="/gui2/?type=authors&mode=browse&sel=10029872" target="_blank">Horváth András (<span class="authorship-author-name">Horváth András</span> <span class="authorAux-mtmt"> mérnöki tudományok</span>) </a> </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10065329"><a href="/gui2/?type=authors&mode=browse&sel=10065329" target="_blank">Pétervári Mátyás (<span class="authorship-author-name">Pétervári Mátyás</span> <span class="authorAux-mtmt"> Farmakológia</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/Farmakológiai és Farmakoterápiás Intézet; <span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/<span title="Farmakológiai és Farmakoterápiás Intézet">FFI</span>/MTA-SE Rendszerfarmakológiai Kutatócsoport</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="1035707"><a href="/gui2/?type=authors&mode=browse&sel=1035707" target="_blank">Csermely Péter (<span class="authorship-author-name">Csermely Péter</span> <span class="authorAux-mtmt"> Biokémia</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/<span title="Biokémiai és Molekuláris Biológiai Intézet">BMBI</span>/Molekuláris Biológiai Tanszék</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10010125"><a href="/gui2/?type=authors&mode=browse&sel=10010125" target="_blank">Ferdinandy Péter (<span class="authorship-author-name">Ferdinandy Péter</span> <span class="authorAux-mtmt"> Farmakológia, molekuláris medicina</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/Farmakológiai és Farmakoterápiás Intézet; <span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/<span title="Farmakológiai és Farmakoterápiás Intézet">FFI</span>/MTA-SE Rendszerfarmakológiai Kutatócsoport</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10057401"><a href="/gui2/?type=authors&mode=browse&sel=10057401" target="_blank">Ágg Bence ✉ (<span class="authorship-author-name">Ágg Bence</span> <span class="authorAux-mtmt"> Farmakológia</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/Farmakológiai és Farmakoterápiás Intézet; <span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Intézet">I</span>/<span title="Farmakológiai és Farmakoterápiás Intézet">FFI</span>/MTA-SE Rendszerfarmakológiai Kutatócsoport</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;32690009" target="_blank">Efficient link prediction in the protein–protein interaction network using topological information in a generative adversarial network machine learning model</a></div> <div> <span class="journal-title">BMC BIOINFORMATICS</span> <span class="journal-issn">(<a target="_blank" href="https://portal.issn.org/resource/ISSN/1471-2105">1471-2105</a> <a target="_blank" href="https://portal.issn.org/resource/ISSN/1471-2105">1471-2105</a>)</span>: <span class="journal-volume">23</span> <span class="journal-issue">1</span> <span class="page"> Paper 78. 19 p. </span> <span class="year">(2022)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_GOLD" title=" Gold "> <a style="color:blue" title="10.1186/s12859-022-04598-x" target="_blank" href="https://doi.org/10.1186/s12859-022-04598-x"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="000757934300002" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/000757934300002"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="85125020446" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85125020446"> Scopus </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="35183129" target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=35183129&dopt=Abstract"> PubMed </a> </span> </span> <OnlyViewableByAuthor><div class="ratings"> <div class="journal-subject">Folyóirat szakterülete: Scopus - Applied Mathematics&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q1</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Biochemistry&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q1</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Computer Science Applications&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q1</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Molecular Biology&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q2</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Structural Biology&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q2</div> </div></OnlyViewableByAuthor> <div class="publication-citation" style="margin-left: 0.5cm;"> <span title="Nyilvános idézőközlemények összesen, említések nélkül" class="citingPub-count">Nyilvános idéző összesen: 42</span> | Független: 40 | Függő: 2 | Nem jelölt: 0 | WoS jelölt: 30 | Scopus jelölt:&nbsp;35 | WoS/Scopus jelölt:&nbsp;39 | DOI jelölt:&nbsp;37 </div> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;32690009&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 1 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 32690009</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> Forrás Idéző | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">kézi felvitel</span> </div> <div class="funder"> (Open access funding of Semmelweis University), National Heart Program(NVKP-16-1-2016-0017) Támogató: NKFIH, (K131458), Thematic Excellence Program (Semmelweis University)(2020-4.1.1.-TKP2020) Támogató: Innovációs és Technológiai Minisztérium, Thematic Institutional Excellence Programme(TKP2021-EGA-24) Támogató: Emberi Erőforrások Minisztériuma, (ÚNKP-20-4-I-SE-7), (ÚNKP-21-4-II-SE-18), Az orvos-, egészségtudományi- és gyógyszerészképzés tudományos műhelyeinek fejlesztése(EFOP-3.6.3-VEKOP-16-2017-00009) Támogató: EFOP-VEKOP, (Semmelweis 250+ Kiválósági PhD Ösztöndíj) </div> <div class="lastModified">Utolsó módosítás: 2025.03.06. 23:01 Horváth András (mérnöki tudományok) </div> <pre class="comment" style="margin-top: 0; margin-bottom: 0;"><u>Megjegyzés</u>: Funding Agency and Grant Number: Semmelweis University; National Research, Development and Innovation Office of Hungary (NKFIA) [NVKP-16-1-2016-0017, K131458]; Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary of the Semmelweis University [2020-4.1.1.-TKP2020, TKP2021-EGA-24]; Research Excellence Programme of the National Research, Development and Innovation...</pre> </div></div>