{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T23:51:45Z","timestamp":1781653905472,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100018777","name":"Nile University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100018777","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Alzheimer\u2019s disease (AD) is a complicated disease that attacks the brain\u2019s neurons. Numerous efforts have been directed towards identifying interactions of single-nucleotide polymorphisms (SNPs). Nevertheless, the large volume of SNP data leads to an explosion of high-order SNP combinations, which substantially limits the effectiveness of interaction detection. Consequently, investigating SNP-SNP interactions is crucial in precision medicine (PM). This paper presents two frameworks for identifying and visualizing SNP-SNP interactions associated with AD risk. The first framework aims to integrate ensemble learning techniques and multifactor dimensionality reduction (MDR). The promising outcomes of this framework presented significant risk genes and SNP-SNP interactions with high accuracy. The achieved classification accuracy of 5-way interaction models was 0.874. The accuracy of the 2-way, 3-way, and 4-way models was 0.6648, 0.7169, and 0.7878, respectively. In the second framework, a deep neural network (DNN) is employed with SHapley Additive exPlanations (SHAP) to identify the most highly ranked SNPs that suggest significant SNP-SNP interactions that could aid in interpreting AD risk. The classification accuracy of 5-way interaction models was 0.8. The classification accuracy of the pairwise, 3-way, and 4-way models was 0.65, 0.7, and 0.77, respectively. This study identifies potential risk genes and SNP-SNP interactions associated with AD risk. This work shows that LOC105374292, NSUN7, LOC101929507, and LINC01482 are four novel genes associated with AD by both proposed frameworks.<\/jats:p>","DOI":"10.1007\/s12559-026-10572-z","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T20:02:13Z","timestamp":1777406533000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computational Frameworks for Identifying and Visualizing SNP-SNP Interactions in Alzheimer\u2019s Disease Risk"],"prefix":"10.1007","volume":"18","author":[{"given":"Marwa M. Abd El","family":"Hamid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mai S.","family":"Mabrouk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"issue":"24","key":"10572_CR1","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.3390\/molecules25245789","volume":"25","author":"Z Breijyeh","year":"2020","unstructured":"Breijyeh Z, Karaman R. Comprehensive review on Alzheimer\u2019s disease: causes and treatment. Molecules. 2020;25(24):5789.","journal-title":"Molecules"},{"issue":"4","key":"10572_CR2","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.jalz.2012.04.007","volume":"8","author":"M Carrillo","year":"2012","unstructured":"Carrillo Met el. Alzheimer\u2019s Dement. 2012;8(4):337\u201342. Worldwide Alzheimer\u2019s disease neuroimaging initiative.","journal-title":"Alzheimer\u2019s Dement"},{"key":"10572_CR3","doi-asserted-by":"crossref","unstructured":"Abd El Hamid M. et el., Machine learning for detecting epistasis interactions and its relevance to personalized medicine in Alzheimer\u2019s disease: systematic review. Biomed Eng Appl, Basis Commun. 2021;33(6).","DOI":"10.4015\/S1016237221500472"},{"key":"10572_CR4","unstructured":"Li K et al. Utilizing deep learning to optimize software development processes; 2024. arXiv preprint arXiv:2404.13630."},{"key":"10572_CR5","doi-asserted-by":"publisher","first-page":"101673","DOI":"10.1016\/j.genrep.2022.101673","volume":"29","author":"M Abd El Hamid","year":"2022","unstructured":"Abd El Hamid M, Omar Y, Shaheen M, Mabrouk M. Discovering epistasis interactions in Alzheimer\u2019s disease using deep learning model. Gene Rep. 2022;29:101673.","journal-title":"Gene Rep"},{"issue":"5","key":"10572_CR6","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1111\/jdi.12830","volume":"9","author":"F Xie","year":"2018","unstructured":"Xie F, Chan J, Ma R. Precision medicine in diabetes prevention, classification and management. J Diabetes Invest. 2018;9(5):998\u20131015.","journal-title":"J Diabetes Invest"},{"issue":"05","key":"10572_CR7","first-page":"1950040","volume":"31","author":"M Abd El Hamid","year":"2019","unstructured":"Abd El Hamid M, Mabrouk M, Omar Y. Developing an early predictive system for identifying genetic biomarkers associated to Alzheimer\u2019s disease using machine learning techniques. Biomedical Engineering: Appl Basis Commun. 2019;31(05):1950040.","journal-title":"Biomedical Engineering: Appl Basis Commun"},{"issue":"6","key":"10572_CR8","doi-asserted-by":"publisher","first-page":"e20133","DOI":"10.1371\/journal.pone.0020133","volume":"6","author":"B Lehne","year":"2011","unstructured":"Lehne B, Lewis CM, Schlitt T. From SNPs to genes: Disease association at the gene level. PLoS ONE. 2011;6(6):e20133.","journal-title":"PLoS ONE"},{"key":"10572_CR9","doi-asserted-by":"crossref","unstructured":"Mostafa M, Omar Y, Mabrouk M. Identifying genetic biomarkers associated to Alzheimer\u2019s disease using support vector machine. In: 2016 8th Cairo International Biomedical Engineering Conference (CIBEC). IEEE; 2016. pp. 5\u20139.","DOI":"10.1109\/CIBEC.2016.7836087"},{"issue":"12","key":"10572_CR10","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1111\/febs.13307","volume":"282","author":"A Travers","year":"2015","unstructured":"Travers A, Muskhelishvili G. DNA structure and function. FEBS J. 2015;282(12):2279\u201395.","journal-title":"FEBS J"},{"issue":"11","key":"10572_CR11","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/s100380200086","volume":"47","author":"B Shastry","year":"2002","unstructured":"Shastry B. SNP alleles in human disease and evolution. J Hum Genet. 2002;47(11):561\u20136.","journal-title":"J Hum Genet"},{"key":"10572_CR12","doi-asserted-by":"crossref","unstructured":"Kim J. et al. SNP selection in genome-wide association studies via penalized support vector machine with MAX test. Comput Math Meth Med. 2013;2013.","DOI":"10.1155\/2013\/340678"},{"key":"10572_CR13","doi-asserted-by":"crossref","unstructured":"Abd El Hamid M, Shaheen M, Omar Y, Mabrouk M. Discovering epistasis interactions in Alzheimer\u2019s disease using integrated framework of ensemble learning and multifactor dimensionality reduction (MDR). Ain Shams Eng J. 2022;14 (7):101986.","DOI":"10.1016\/j.asej.2022.101986"},{"issue":"2","key":"10572_CR14","doi-asserted-by":"publisher","first-page":"54","DOI":"10.9758\/cpn.2011.9.2.54","volume":"9","author":"S Ma","year":"2011","unstructured":"Ma S, Lam L. Panel of genetic variations as a potential non-invasive biomarker for early diagnosis of Alzheimer\u2019s disease. Clin Psychopharmacol Neurosci. 2011;9(2):54.","journal-title":"Clin Psychopharmacol Neurosci"},{"key":"10572_CR15","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3389\/fgene.2019.00267","volume":"10","author":"D Ho","year":"2019","unstructured":"Ho D, Schierding W, Wake M, O\u2019Sullivan J. Machine learning SNP based prediction for precision medicine. Front Genet. 2019;10:267.","journal-title":"Front Genet"},{"key":"10572_CR16","doi-asserted-by":"publisher","first-page":"2463","DOI":"10.1093\/hmg\/11.20.2463","volume":"11","author":"HJ Cordell","year":"2002","unstructured":"Cordell HJ. Epistasis: what it means, what it doesn\u2019t mean, and statistical methods to detect it in humans. Hum Mol Genet. 2002;11:2463\u20138.","journal-title":"Hum Mol Genet"},{"key":"10572_CR17","doi-asserted-by":"publisher","first-page":"e5854","DOI":"10.7717\/peerj.5854","volume":"6","author":"F Dorani","year":"2018","unstructured":"Dorani F, Hu T, Woods MO, Zhai G. Ensemble learning for detecting gene-gene interactions in colorectal cancer. PeerJ. 2018;6:e5854.","journal-title":"PeerJ"},{"key":"10572_CR18","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.neubiorev.2019.06.018","volume":"103","author":"AR Dunn","year":"2019","unstructured":"Dunn AR, O\u2019Connell KM, Kaczorowski CC. Gene-by-environment interactions in Alzheimer\u2019s disease and Parkinson\u2019s disease. Neurosci Biobehavioral Reviews. 2019;103:73\u201380.","journal-title":"Neurosci Biobehavioral Reviews"},{"issue":"1","key":"10572_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-021-00243-0","volume":"14","author":"A Orlenko","year":"2021","unstructured":"Orlenko A, Moore J. A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions. BioData Min. 2021;14(1):1\u201317.","journal-title":"BioData Min"},{"issue":"1","key":"10572_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-11270-0","volume":"12","author":"S D\u2019Silva","year":"2022","unstructured":"D\u2019Silva S, Chakraborty S, Kahali B. Concurrent outcomes from multiple approaches of epistasis analysis for human body mass index associated loci provide insights into obesity biology. Sci Rep. 2022;12(1):1\u201314.","journal-title":"Sci Rep"},{"issue":"6","key":"10572_CR21","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.18632\/aging.203984","volume":"14","author":"A Petrelis","year":"2022","unstructured":"Petrelis A, Stathopoulou M, Kafyra M, Murray H, Masson C, Lamont J, Visvikis-Siest S. VEGF-A-related genetic variants protect against Alzheimer\u2019s disease. Aging. 2022;14(6):2524.","journal-title":"Aging"},{"key":"10572_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Q, et al. Alzheimer\u2019s disease risk genes mining based on a supervised machine learning method and ppi network construction. CSCWD. IEEE; 2024.","DOI":"10.1109\/CSCWD61410.2024.10580265"},{"key":"10572_CR23","unstructured":"Jin Z et al. Mutual learning for joint disease detection and severity prediction reveals multimodal pathogenesis for neurodegenerative disorders. Bioinformatics. 2025;42(1)."},{"key":"10572_CR24","doi-asserted-by":"crossref","unstructured":"Yu S et al. Synergistic integration of clinical and multi-omics data for early MCI diagnosis using an attention-based graph fusion network. J Neurosci Meth. 2026;110664.","DOI":"10.1016\/j.jneumeth.2025.110664"},{"key":"10572_CR25","doi-asserted-by":"crossref","unstructured":"Liu H-J et al. Potential synthetic associations created by epistasis. Genome Biol. 2025;26(336).","DOI":"10.1186\/s13059-025-03807-z"},{"issue":"10","key":"10572_CR26","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1007\/s00439-012-1188-9","volume":"131","author":"A Ziegler","year":"2012","unstructured":"Ziegler A, Koch A, Krockenberger K. Personalized medicine using DNA biomarkers: A review. Hum Genet. 2012;131(10):1627\u201338.","journal-title":"Hum Genet"},{"key":"10572_CR27","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.3389\/fneur.2019.01162","volume":"10","author":"H Chen","year":"2019","unstructured":"Chen H, He Y, Ji J, Shi Y. A machine learning method for identifying critical interactions between gene pairs in Alzheimer\u2019s disease prediction. Front Neurol. 2019;10:1162.","journal-title":"Front Neurol"},{"key":"10572_CR28","doi-asserted-by":"crossref","unstructured":"Petersen RC, Aisen PS, Beckett LA. Alzheimer\u2019s disease neuroimaging initiative (ADNI) clinical characterization. Neurology. 2010;74(3).","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"10572_CR29","unstructured":"Purcell S. PLINK (1.07). Documentation, ed. 2012."},{"key":"10572_CR30","doi-asserted-by":"crossref","unstructured":"Dietterich T. Ensemble methods in machine learning. In: International workshop on multiple classifier systems; 2000. pp. 1\u201315.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"10572_CR31","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.jbi.2018.07.015","volume":"85","author":"R Urbanowicz","year":"2018","unstructured":"Urbanowicz R, Olson R, Schmitt P, Meeker M, Moore H. Benchmarking relief-based feature selection methods for bioinformatics data mining. J Biomed Inform. 2018;85:168\u201388.","journal-title":"J Biomed Inform"},{"issue":"1","key":"10572_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-10-213","volume":"10","author":"B Menze","year":"2009","unstructured":"Menze B, Kelm B, Masuch R, Hamprecht F. A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics. 2009;10(1):1\u201316.","journal-title":"BMC Bioinformatics"},{"issue":"10","key":"10572_CR33","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","volume":"26","author":"A Altmann","year":"2010","unstructured":"Altmann A, Tolo\u015fi L, Sander O, Lengauer T. Permutation importance: A corrected feature importance measure. Bioinformatics. 2010;26(10):1340\u20137.","journal-title":"Bioinformatics"},{"key":"10572_CR34","doi-asserted-by":"crossref","unstructured":"Chen T. and Guestrin C. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining; 2016. pp. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"10572_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12879-016-1839-x","volume":"16","author":"R Zimmerman","year":"2016","unstructured":"Zimmerman R, Balasubramani G, Eng P, H., and, Wisniewski S. Classification and Regression Tree (CART) analysis to predict influenza in primary care patients. BMC Infect Dis. 2016;16(1):1\u201311.","journal-title":"BMC Infect Dis"},{"key":"10572_CR36","doi-asserted-by":"crossref","unstructured":"Pers TH et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6(5890).","DOI":"10.1038\/ncomms6890"},{"key":"10572_CR37","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/nar\/29.1.308","volume":"29","author":"ST Sherry","year":"2001","unstructured":"Sherry ST, Ward M-H, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308\u201311.","journal-title":"Nucleic Acids Res"},{"key":"10572_CR38","unstructured":"Schwing AG, Urtasun R. Fully connected deep structured networks; 2015. arXiv preprint arXiv:1503.02351."},{"key":"10572_CR39","doi-asserted-by":"crossref","unstructured":"Dickinson Q, Meyer JG. Positional SHAP for interpretation of deep learning models trained from biological sequences. bioRxiv; 2021.","DOI":"10.1101\/2021.03.04.433939"},{"key":"10572_CR40","doi-asserted-by":"crossref","unstructured":"Cui T, El Mekkaoui K, Reinvall J, Havulinna AS, Marttinen P, Kaski S. Gene-gene interaction detection with deep learning. bioRxiv; 2021.","DOI":"10.1101\/2021.03.12.435063"},{"key":"10572_CR41","doi-asserted-by":"crossref","unstructured":"Lee Y, Oh J, Kim D, Kim G. SHAP value-based feature importance analysis for short-term load forecasting. J Electr Eng Technol. 2022;18(1):1\u201310.","DOI":"10.1007\/s42835-022-01161-9"},{"key":"10572_CR42","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1002\/gepi.20211","volume":"31","author":"DR Velez","year":"2007","unstructured":"Velez DR, et al. A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genetic Epidemiology: Official Publication Int Genetic Epidemiol Soc. 2007;31:306\u201315.","journal-title":"Genetic Epidemiology: Official Publication Int Genetic Epidemiol Soc"},{"key":"10572_CR43","doi-asserted-by":"crossref","unstructured":"Wang H et al. Genome-wide epistasis analysis for Alzheimer\u2019s disease and implications for genetic risk prediction. Alzheimer\u2019s Res Ther. 2021;13(55).","DOI":"10.1186\/s13195-021-00794-8"},{"key":"10572_CR44","doi-asserted-by":"crossref","unstructured":"Thomas W, Sreekanth VR, Ali A. Biomarkers for identifying individuals at risk of Alzheimer disease. Int J Emerg Mental Health Hum Resil. 2018;20:1\u2013390.","DOI":"10.4172\/1522-4821.1000390"},{"issue":"3","key":"10572_CR45","first-page":"656","volume":"10","author":"X Zhang","year":"2013","unstructured":"Zhang X, Chan TSM, Tsang KWK, Li H. A Bayesian partitioning approach for detecting pleiotropic and epistatic effects in genome-wide association studies. IEEE\/ACM Trans Comput Biol Bioinf. 2013;10(3):656\u201367.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"D1","key":"10572_CR46","doi-asserted-by":"publisher","first-page":"D36","DOI":"10.1093\/nar\/gks1195","volume":"41","author":"DA Benson","year":"2012","unstructured":"Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2012;41(D1):D36\u201342.","journal-title":"Nucleic Acids Res"},{"key":"10572_CR47","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/s12031-020-01633-5","volume":"71","author":"A Koijam","year":"2021","unstructured":"Koijam A, Singh, et al. Association of dopamine transporter gene with heroin dependence in an Indian subpopulation from Manipur. J Mol Neurosci. 2021;71:122\u201336.","journal-title":"J Mol Neurosci"},{"key":"10572_CR48","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1038\/ng0504-431","volume":"36","author":"KG Becker","year":"2004","unstructured":"Becker KG, et al. The genetic association database. Nat Genet. 2004;36:431\u20132.","journal-title":"Nat Genet"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-026-10572-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-026-10572-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-026-10572-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:53:34Z","timestamp":1781650414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-026-10572-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["10572"],"URL":"https:\/\/doi.org\/10.1007\/s12559-026-10572-z","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]},"assertion":[{"value":"10 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}}],"article-number":"39"}}