Heap,G.A., Trynka,G., Jansen,R.C., Bruinenberg,M., Swertz,M.A., Dinesen,L.C., Hunt,K.A., Wijmenga,C., Vanheel,D.A. and Franke,L. (2009) Complex nature of SNP genotype effects on gene expression in primary human leucocytes. BMC Med. Genom., 2, 1.
Bush,W.S. and Moore,J.H. (2012) Chapter 11: Genome-wide association studies. PLoS Comput. Biol., 8, e1002822.
MacArthur,J., Bowler,E., Cerezo,M., Gil,L., Hall,P., Hastings,E., Junkins,H., McMahon,A., Milano,A., Morales,J., et al. (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res., 45, D896–D901.
Gibson,G. (2012) Rare and common variants: twenty arguments. Nat. Rev. Genet., 13, 135–145.
Lippert,C., Listgarten,J., Davidson,R.I., Baxter,S., Poon,H., Kadie,C.M. and Heckerman,D. (2013) An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data. Sci. Rep., 3, 1099.
Manolio,T.A., Collins,F.S., Cox,N.J., Goldstein,D.B., Hindorff,L.A., Hunter,D.J., McCarthy,M.I., Ramos,E.M., Cardon,L.R., Chakravarti,A., et al. (2009) Finding the missing heritability of complex diseases. Nature, 461, 747–753.
Sherry,S.T., Ward,M.H., Kholodov,M., Baker,J., Phan,L., Smigielski,E.M. and Sirotkin,K. (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res., 29, 308–311.
Blumenthal,D.B., Baumbach,J., Hoffmann,M., Kacprowski,T. and List,M. (2020) A framework for modeling epistatic interaction. Bioinformatics, 37, 1708–1716.
Caylak,G., Tastan,O. and Cicek,A.E. (2020) Potpourri: an epistasis test prioritization algorithm via diverse SNP selection. J. Comput. Biol., 28, 365–377.
Cowman,T. and Koyutürk,M. (2017) Prioritizing tests of epistasis through hierarchical representation of genomic redundancies. Nucleic Acids Res., 45, e131.
Ayati,M. and Koyutürk,M. (2014) Prioritization of genomic locus pairs for testing epistasis. In: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB’14. Association for Computing Machinery, NY, pp. 240–248.
Jing,P.-J. and Shen,H.-B. (2015) MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. Bioinformatics, 31, 634–641.
Duroux,D., Climente-González,H., Azencott,C.-A. and Van Steen,K. (2022) Interpretable network-guided epistasis detection. Gigascience, 11, giab093.
Banchi,L., Fingerhuth,M., Babej,T., Ing,C. and Arrazola,J.M. (2020) Molecular docking with Gaussian boson sampling. Sci. Adv., 6, eaax1950.
Boev,A., Rakitko,A., Usmanov,S., Kobzeva,A., Popov,I., Ilinsky,V., Kiktenko,E. and Fedorov,A. (2021) Genome assembly using quantum and quantum-inspired annealing. Sci. Rep., 11, 13183.
Nałecz-Charkiewicz,K. and Nowak,R.M. (2022) Algorithm for DNA sequence assembly by quantum annealing. BMC Bioinformatics, 23, 122.
Sarkar,A., Al-Ars,Z. and Bertels,K. (2021) QuASeR: Quantum Accelerated de novo DNA sequence reconstruction. PLoS One, 16, e0249850.
Vakili,M.G., Gorgulla,C., Nigam,A., Bezrukov,D., Varoli,D., Aliper,A., Polykovsky,D., Padmanabha Das,K.M., Snider,J., Lyakisheva,A., et al. (2024) Quantum computing-enhanced algorithm unveils novel inhibitors for KRAS. arXiv doi: https://arxiv.org/abs/2402.08210, 13 february 2024, preprint: not peer reviewed.
Siek,J., Lumsdaine,A. and Lee,L.-Q. (2002) The Boost Graph Library: User Guide and Reference Manual. Addison-Wesley.
Csardi,G., Nepusz,T. and Others (2006) The igraph software package for complex network research. InterJournal, Complex Syst., 1695, 1–9.
Liu,F. and Chaudhary,V. (2003) A practical OpenMP compiler for system on chips. In: OpenMP Shared Memory Parallel Programming. Springer, Berlin, Heidelberg, pp. 54–68.
Marees,A.T., de Kluiver,H., Stringer,S., Vorspan,F., Curis,E., Marie-Claire,C. and Derks,E.M. (2018) A tutorial on conducting genome-wide association studies: quality control and statistical analysis. Int. J. Methods Psychiatr. Res., 27, e1608.
Kunkle,B.W., Grenier-Boley,B., Sims,R., Bis,J.C., Damotte,V., Naj,A.C., Boland,A., Vronskaya,M., van der Lee,S.J., Amlie-Wolf,A., et al. (2019) Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet., 51, 414–430.
Wellcome Trust Case Control Consortium, Craddock,N., Hurles,M.E., Cardin,N., Pearson,R.D., Plagnol,V., Robson,S., Vukcevic,D., Barnes,C., Conrad,D.F., et al. (2010) Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature, 464, 713–720.
Bycroft,C., Freeman,C., Petkova,D., Band,G., Elliott,L.T., Sharp,K., Motyer,A., Vukcevic,D., Delaneau,O., O’Connell,J., et al. (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature, 562, 203–209.
World Health Organization (1993) In: The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research, World Health Organization.
Best,D. and Roberts,D. (1975) Algorithm AS 89: the upper tail probabilities of Spearman’s rho. J. Roy. Stat. Soc. Ser. C (Appl. Stat.), 24, 377–379.
Caylak,G., Tastan,O. and Cicek,A.E. (2021) Potpourri: an epistasis test prioritization algorithm via diverse SNP Selection. J. Comput. Biol., 28, 365–377.
Cowman,T. and Koyutürk,M. (2017) Prioritizing tests of epistasis through hierarchical representation of genomic redundancies. Nucleic Acids Res., 45, e131.
Guan,B., Zhao,Y. and Sun,W. (2018) Ant colony optimization with an automatic adjustment mechanism for detecting epistatic interactions. Comput. Biol. Chem., 77, 354–362.
Guan,B. and Zhao,Y. (2019) Self-adjusting ant colony optimization based on information entropy for detecting epistatic interactions. Genes, 10, 114.
Schüpbach,T., Xenarios,I., Bergmann,S. and Kapur,K. (2010) FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics, 26, 1468–1469.
Wang,Y., Liu,X., Robbins,K. and Rekaya,R. (2010) AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Research Notes, 3, 117.
Cao,X., Yu,G., Ren,W., Guo,M. and Wang,J. (2019) DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction. Hum. Mutat., 41, 719–734.
Gola,D., John,J. M.M., van Steen,K. and König,I.R. (2015) A roadmap to multifactor dimensionality reduction methods. Brief. Bioinform., 17, 293–308.
Yu,W., Lee,S. and Park,T. (2016) A unified model based multifactor dimensionality reduction framework for detecting gene–gene interactions. Bioinformatics, 32, i605–i610.
Ritchie,M.D., Hahn,L.W., Roodi,N., Bailey,L.R., Dupont,W.D., Parl,F.F. and Moore,J.H. (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet., 69, 138–147.
Sinnott-Armstrong,N.A., Greene,C.S. and Moore,J.H. (2010) Fast genome-wide epistasis analysis using ant colony optimization for multifactor dimensionality reduction analysis on graphics processing units. In: Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO’10. ACM Press.
Ansarifar,J. and Wang,L. (2019) New algorithms for detecting multi-effect and multi-way epistatic interactions. Bioinformatics, 35, 5078–5085.
Wu,T.T., Chen,Y.F., Hastie,T., Sobel,E. and Lange,K. (2009) Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics, 25, 714–721.
North,B.V., Curtis,D. and Sham,P.C. (2005) Application of logistic regression to case-control association studies involving two causative loci. Hum. Hered., 59, 79–87.
Kerimov,N., Hayhurst,J.D., Peikova,K., Manning,J.R., Walter,P., Kolberg,L., Samoviča,M., Sakthivel,M.P., Kuzmin,I., Trevanion,S.J., et al. (2021) A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet., 53, 1290–1299.
Durinck,S., Spellman,P.T., Birney,E. and Huber,W. (2009) Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc., 4, 1184–1191.
Benjamini,Y. and Hochberg,Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc..125, 289–300.
Kirkpatrick,S., Gelatt,C.D. and Vecchi,M.P. (1983) Optimization by simulated annealing. Science, 220, 671–680.
Riesen,K., Fischer,A. and Bunke,H. (2017) Improved graph edit distance approximation with simulated annealing. In: Foggia,P., Liu,C.-L. and Vento,M. (eds.) GbRPR 2017, Vol. 10310 of LNCS. Springer, Cham, pp. 222–231.
Blumenthal,D.B., Bougleux,S., Gamper,J. and Brun,L. (2019) GEDLIB: A C++ library for graph edit distance computation. In: Conte,D., Ramel,J.-Y. and Foggia,P. (eds.) GbRPR 2019, Vol. 11510 of LNCS. Springer, Cham, pp. 14–24.
Blumenthal,D.B., Boria,N., Gamper,J., Bougleux,S. and Brun,L. (2020) Comparing heuristics for graph edit distance computation. VLDB J., 29, 419–458.
Dorigo,M., Birattari,M. and Stutzle,T. (2006) Ant colony optimization. IEEE Comput. Intell. Mag., 1, 28–39.
Koza,J.R. and Poli,R. (2005) Genetic Programming. Springer, US, Boston, MA, pp. 127–164.
Duarte,A., Sánchez-Oro,J., Mladenović,N. and Todosijević,R. (2018) Variable Neighborhood Descent. Springer International Publishing, Cham, pp. 341–367.
Boria,N., Blumenthal,D.B., Bougleux,S. and Brun,L. (2019) Improved local search for graph edit distance. Pattern Recognit. Lett., 129, 19–25.
Lazareva,O., Baumbach,J., List,M. and Blumenthal,D.B. (2021) On the limits of active module identification. Brief. Bioinform., 22, bbab066.
Subramanian,A., Tamayo,P., Mootha,V.K., Mukherjee,S., Ebert,B.L., Gillette,M.A., Paulovich,A., Pomeroy,S.L., Golub,T.R., Lander,E.S., et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A., 102, 15545–15550.
Mootha,V.K., Lindgren,C.M., Eriksson,K.-F., Subramanian,A., Sihag,S., Lehar,J., Puigserver,P., Carlsson,E., Ridderstråle,M., Laurila,E., et al. (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet., 34, 267–273.
Grover,L.K. (1997) Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett., 79, 325.
Roland,J. and Cerf,N.J. (2002) Quantum search by local adiabatic evolution. Phys. Rev. A, 65, 042308.
Pirnay,N., Ulitzsch,V., Wilde,F., Eisert,J. and Seifert,J.-P. (2024) An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory, Sci. Adv., 10, eadj5170.
Aaronson,S. (2022) How Much structure is needed for huge quantum speedups? arXiv doi: https://arxiv.org/abs/2209.06930, 14 September 2022, preprint: not peer reviewed.
King,A.D., Raymond,J., Lanting,T., Harris,R., Zucca,A., Altomare,F., Berkley,A.J., Boothby,K., Ejtemaee,S., Enderud,C., et al. (2023) Quantum critical dynamics in a 5,000-qubit programmable spin glass. Nature, 617, 16–66.
Incudini,M., Tarocco,F., Mengoni,R., Di Pierro,A. and Mandarino,A. (2022) Computing graph edit distance on quantum devices. Quant. Mach. Intell., 4, 24.
Kirkpatrick,S., Gelatt,C.D. and Vecchi,M.P. (1983) Optimization by simulated annealing. science, 220, 671–680.
Earl,D.J. and Deem,M.W. (2005) Parallel tempering: theory, applications, and new perspectives. Phys. Chem. Chem. Phys., 7, 3910–3916.
Traag,V.A., Waltman,L. and van Eck,N.J. (2019) From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep., 9, 5233.
Kanehisa,M. (2002) The KEGG database. Novartis Found. Symp., 247, 91–101.
Guo,X., Qiu,W., Garcia-Milian,R., Lin,X., Zhang,Y., Cao,Y., Tan,Y., Wang,Z., Shi,J., Wang,J., et al. (2017) Genome-wide significant, replicated and functional risk variants for Alzheimer’s disease. J. Neural. Transm., 124, 1455–1471.
Kulminski,A.M., Shu,L., Loika,Y., He,L., Nazarian,A., Arbeev,K., Ukraintseva,S., Yashin,A. and Culminskaya,I. (2020) Genetic and regulatory architecture of Alzheimer’s disease in the APOE region. Alzheimers. Dement., 12, e12008.
Thul,P.J. and Lindskog,C. (2018) The human protein atlas: a spatial map of the human proteome. Protein Sci., 27, 233–244.
Braak,H. and Braak,E. (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol., 82, 239–259.
Braak,H., Thal,D.R., Ghebremedhin,E. and Del Tredici,K. (2011) Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J. Neuropathol. Exp. Neurol., 70, 960–969.
Thal,D.R., Rüb,U., Orantes,M. and Braak,H. (2002) Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology, 58, 1791–1800.
Filip,P., Canna,A., Moheet,A., Bednarik,P., Grohn,H., Li,X., Kumar,A.F., Olawsky,E., Eberly,L.E., Seaquist,E.R., et al. (2020) Structural alterations in deep brain structures in type 1 diabetes. Diabetes, 69, 2458–2466.
Knapp,M., Tu,X. and Wu,R. (2019) Vascular endothelial dysfunction, a major mediator in diabetic cardiomyopathy. Acta Pharmacol. Sin., 40, 1–8.
Eizirik,D.L., Pasquali,L. and Cnop,M. (2020) Pancreatic β-cells in type 1 and type 2 diabetes mellitus: different pathways to failure. Nat. Rev. Endocrinol., 16, 349–362.
Gillespie,K.M. (2006) Type 1 diabetes: pathogenesis and prevention. CMAJ, 175, 165–170.
Granlund,L., Hedin,A., Korsgren,O., Skog,O. and Lundberg,M. (2022) Altered microvasculature in pancreatic islets from subjects with type 1 diabetes. PLoS One, 17, e0276942.
Stefański,A., Wolf,J., Harazny,J.M., Miszkowska-Nagórna,E., Wolnik,B., Murawska,J., Narkiewicz,K. and Schmieder,R.E. (2023) Impact of type 1 diabetes and its duration on wall-to-lumen ratio and blood flow in retinal arterioles. Microvasc. Res., 147, 104499.
Kiseleva,E., Ryabkov,M., Baleev,M., Bederina,E., Shilyagin,P., Moiseev,A., Beschastnov,V., Romanov,I., Gelikonov,G. and Gladkova,N. (2021) Prospects of intraoperative multimodal OCT application in patients with acute mesenteric ischemia. Diagnostics (Basel), 11, 705.
Ahmed,M. (2021) Ischemic bowel disease in 2021. World J. Gastroenterol., 27, 4746–4762.
Green,B.T. and Tendler,D.A. (2005) Ischemic colitis: a clinical review. South. Med. J., 98, 217–222.
1000 Genomes Project Consortium, Auton,A., Brooks,L.D., Durbin,R.M., Garrison,E.P., Kang,H.M., Korbel,J.O., Marchini,J.L., McCarthy,S., McVean,G.A., et al. (2015) A global reference for human genetic variation. Nature, 526, 68–74.
Chapuis,G., Djidjev,H., Hahn,G. and Rizk,G. (2019) Finding maximum cliques on the D-wave quantum annealer. J. Signal Process. Syst., 91, 363–377.
Maier,A., Hartung,M., Abovsky,M., Adamowicz,K., Bader,G.D., Baier,S., Blumenthal,D.B., Chen,J., Elkjaer,M.L., Garcia-Hernandez,C., et al. (2024) Drugst.One — a plug-and-play solution for online systems medicine and network-based drug repurposing. Nucleic Acids Res., 52, W481–W488.
Zuk,O., Hechter,E., Sunyaev,S.R. and Lander,E.S. (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl. Acad. Sci., 109, 1193–1198.
Louadi,Z., Yuan,K., Gress,A., Tsoy,O., Kalinina,O.V., Baumbach,J., Kacprowski,T. and List,M. (2021) DIGGER: exploring the functional role of alternative splicing in protein interactions. Nucleic Acids Res., 49, D309–D318.
Hernández-Lorenzo,L., Hoffmann,M., Scheibling,E., List,M., Matías-Guiu,J.A. and Ayala,J.L. (2022) On the limits of graph neural networks for the early diagnosis of Alzheimer’s disease. Sci. Rep., 12, 17632.
Cortazzo,P., Cerveñansky,C., Marín,M., Reiss,C., Ehrlich,R. and Deana,A. (2002) Silent mutations affect in vivo protein folding in Escherichia coli. Biochem. Biophys. Res. Commun., 293, 537–541.
Watanabe,K., Stringer,S., Frei,O., Umićević Mirkov,M., de Leeuw,C., Polderman,T.J., van der Sluis,S., Andreassen,O.A., Neale,B.M. and Posthuma,D. (2019) A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet., 51, 1339–1348.
Tate,R., Moondra,J., Gard,B., Mohler,G. and Gupta,S. (2023) Warm-started QAOA with custom mixers provably converges and computationally beats Goemans-Williamson’s Max-Cut at low circuit depths. Quantum, 7, 1121.