{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:29:33Z","timestamp":1787689773601,"version":"build-2784847793"},"reference-count":131,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype\u2013phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype\u2013genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype\u2013genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.<\/jats:p>","DOI":"10.3390\/cancers16112138","type":"journal-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T07:39:49Z","timestamp":1717486789000},"page":"2138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2593-9810","authenticated-orcid":false,"given":"Hamidreza","family":"Ashayeri","sequence":"first","affiliation":[{"name":"Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Navid","family":"Sobhi","sequence":"additional","affiliation":[{"name":"Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-2801","authenticated-orcid":false,"given":"Pawe\u0142","family":"P\u0142awiak","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland"},{"name":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba\u0142tycka 5, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2974-1801","authenticated-orcid":false,"given":"Siamak","family":"Pedrammehr","sequence":"additional","affiliation":[{"name":"Faculty of Design, Tabriz Islamic Art University, Tabriz 5164736931, Iran"},{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, VIC 3216, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0898-5054","authenticated-orcid":false,"given":"Roohallah","family":"Alizadehsani","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, VIC 3216, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4922-1923","authenticated-orcid":false,"given":"Ali","family":"Jafarizadeh","sequence":"additional","affiliation":[{"name":"Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran"},{"name":"Immunology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"ref_1","first-page":"14","article-title":"Introduction to Machine Learning, Neural Networks, and Deep Learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl. 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