Abuasab T, Garcia-Manero G, Short N, Alvarado Y, Issa GC, Islam R, Maiti A, Yilmaz M, Jain N, Masarova L, Kornblau SM, Jabbour E, Pemmaraju N, Montalban-Bravo G, Pierce SA, DiNardo CD, Kadia TM, Daver N, Konopleva M, Ravandi F. Phase 2 Study of ASTX727 (cedazuridine/decitabine) Plus Venetoclax in patients with relapsed/refractory acute myeloid leukemia (AML) or previously untreated, elderly patients with AML unfit for chemotherapy. Blood. 2022;140(Supplement 1):3324–6. https://doi.org/10.1182/blood-2022-158566.
Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33(8):831–8. https://doi.org/10.1038/nbt.3300.
Almeida-Brasil CC, Hanly JG, Urowitz M, et al. Flares after hydroxychloroquine reduction or discontinuation: results from the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort. Ann Rheum Dis. 2022;81:370–8.
Angermueller C, Lee HJ, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18(1):67. https://doi.org/10.1186/s13059-017-1189-z.
Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139(1):4–15. https://doi.org/10.1093/bmb/ldab016.
Ayana G, Dese K, Dereje Y, Kebede Y, Barki H, Amdissa D, Husen N, Mulugeta F, Habtamu B, Choe S-W. Vision-transformer-based transfer learning for mammogram classification. Diagnostics. 2023;13(2):178. https://doi.org/10.3390/diagnostics13020178.
Benfatto S, Sill M, Jones DTW, Pfister SM, Sahm F, von Deimling A, Capper D, Hovestadt V. Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics. Nat Commun. 2025;16(1):1787. https://doi.org/10.1038/s41467-025-57078-0.
Bhatt M, Shende P. Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment. Arch Comput Methods Eng. 2023;30(4):2777–92. https://doi.org/10.1007/s11831-023-09886-0.
Briganti G, Le Moine O. Artificial intelligence in medicine: Today and tomorrow. Front Med. 2020;7:509744. https://doi.org/10.3389/fmed.2020.00027.
Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J. 2021;19:5762–90. https://doi.org/10.1016/j.csbj.2021.10.009.
Chang X, Zheng Y, Xu K. Single-Cell RNA sequencing: Technological progress and biomedical application in cancer research. Mol Biotechnol. 2024;66(7):1497–519. https://doi.org/10.1007/s12033-023-00777-0.
Chiappinelli KB, Strissel PL, Desrichard A, Li H, Henke C, Akman B, Hein A, Rote NS, Cope LM, Snyder A, Makarov V, Buhu S, Slamon DJ, Wolchok JD, Pardoll DM, Beckmann MW, Zahnow CA, Mergoub T, Chan TA, Strick R. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell. 2015;162(5):974–86. https://doi.org/10.1016/j.cell.2015.07.011.
Clapier CR, Cairns BR. The biology of chromatin remodeling complexes. Annu Rev Biochem. 2009;78(1):273–304. https://doi.org/10.1146/annurev.biochem.77.062706.153223.
Classification and diagnosis of diabetes. Standards of medical care in diabetes-2020. Diabetes Care. 2020;43:S14–31. https://doi.org/10.2337/dc20-S002.
Dagnew TM, Tseng C-EJ, Yoo C-H, Makary MM, Goodheart AE, Striar R, Meyer TN, Rattray AK, Kang L, Wolf KA, Fiedler SA, Tocci D, Shapiro H, Provost S, Sultana E, Liu Y, Ding W, Chen P, Kubicki M, Wang C. Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study. IScience. 2024;27(7):110159. https://doi.org/10.1016/j.isci.2024.110159.
De Riso G, Cocozza S. Artificial intelligence for epigenetics: towards personalized medicine. Curr Med Chem. 2021;28(32):6654–74. https://doi.org/10.2174/0929867327666201117142006.
Deichmann U. Chromatin research and epigenetics - historical perspectives, current research, open questions, and misconceptions. Med Res Arch 2023;11(2). https://doi.org/10.18103/mra.v11i2.3600
Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012;9(3):215–6. https://doi.org/10.1038/nmeth.1906.
Falcinelli B, Bulgari R, Nicola S, Benincasa P. The effect of blue: Red light proportion on germination parameters, growth attributes, and quality of borage sprouts. Sci Hortic. 2024;1(336):113399. https://doi.org/10.1016/j.scienta.2024.113399.
Feil R, Fraga M. Epigenetics and the environment: emerging patterns and implications. Nat Rev Genet. 2012;13:97–109. https://doi.org/10.1038/nrg3142.
Grasso C, Butler T, Rhodes K, Quist M, Neff TL, Moore S, Tomlins SA, Reinig E, Beadling C, Andersen M, Corless CL. Assessing copy number alterations in targeted, amplicon-based next-generation sequencing data. J Mol Diagn. 2015;17(1):53–63. https://doi.org/10.1016/j.jmoldx.2014.09.008.
Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A, Takasawa K, Bolatkan A, Shozu K, Dozen A, Machino H, Takahashi S, Asada K, Komatsu M, Sese J, Kaneko S. Application of artificial intelligence technology in oncology: towards the establishment of precision medicine. Cancers (Basel). 2020;12(12):3532. https://doi.org/10.3390/cancers12123532.
Huang S, Nianguang CAI, Penzuti Pacheco P, Narandes S, Wang Y, Wayne XU. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom Proteomics. Int Inst Anticancer Res. 2018;15(1):41–51. https://doi.org/10.21873/cgp.20063
Jones PA, Takai D. The role of DNA methylation in mammalian epigenetics. Science. 2001;293:1068–70. https://doi.org/10.1126/science.1063852.
Jolma A, Kivioja T, Toivonen J, Cheng L, Wei G, Enge M, Taipale M, Vaquerizas JM, Yan J, Sillanpää MJ, Bonke M, Palin K, Talukder S, Hughes TR, Luscombe NM, Ukkonen E, Taipale J. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res. 2010;20(6):861–73. https://doi.org/10.1101/gr.100552.109.
Kerr D, Ostaszkiewicz J, Dunning T, Martin P. The effectiveness of training interventions on nurses' communication skills: a systematic review. Nurse Educ Today. 2020;1(89):104405.
Khanwalker M, Fujita R, Lee J, Wilson E, Ito K, Asano R, Ikebukuro K, LaBelle J, Sode K. Development of a POCT type insulin sensor employing anti-insulin single chain variable fragment based on faradaic electrochemical impedance spectroscopy under single frequency measurement. Biosens Bioelectron. 2022;200:113901. https://doi.org/10.1016/j.bios.2021.113901.
Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol. 2008;26(12):1351–9. https://doi.org/10.1038/nbt.1508.
Kumar A, Dixit S, Srinivasan K, Vincent PD. Personalized cancer vaccine design using AI-powered technologies. Front Immunol. 2024;15:1357217. https://doi.org/10.3389/fimmu.2024.1357217.
Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27(11):1571–2. https://doi.org/10.1093/bioinformatics/btr167.
Laakso M. Biomarkers for type 2 diabetes. Mol Metab. 2019;27:S139–46. https://doi.org/10.1016/j.molmet.2019.06.016.
Lee J, Azamfar M, Singh J, Siahpour S. Integration of digital twin and deep learning in cyber-physical systems: Towards smart manufacturing. IET Collab Intell Manuf. 2020;2(1):34–6. https://doi.org/10.1049/iet-cim.2020.0009.
Libbrecht M, Noble W. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16:321–32. https://doi.org/10.1038/nrg3920.
Lister R, Mukamel EA, Nery JR, Urich M, Puddifoot CA, Johnson ND, Lucero J, Huang Y, Dwork AJ, Schultz MD, Yu M, Tonti-Filippini J, Heyn H, Hu S, Wu JC, Rao A, Esteller M, He C, Haghighi FG, Ecker JR. Global epigenomic reconfiguration during mammalian brain development. Science. 2013;341(6146):1237905. https://doi.org/10.1126/science.1237905.
Liu Y, Siejka-Zielińska P, Velikova G, Bi Y, Yuan F, Tomkova M, Bai C, Chen L, Schuster-Böckler B, Song CX. Bisulfite-free direct detection of 5-methylcytosine and 5-hydroxymethylcytosine at base resolution. Nat Biotechnol. 2019;37(4):424–9. https://doi.org/10.1038/s41587-019-0041-2.
Lu X, Zhao BS, He C. TET family proteins: Oxidation activity, interacting molecules, and functions in diseases. Chem Rev. 2015;115(6):2225–39. https://doi.org/10.1021/cr500470n.
Ma W, Lau Y-L, Yang W, Wang Y-F. Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus. Front Genet. 2022;13:902793. https://doi.org/10.3389/fgene.2022.902793.
Mirza N, Manankil-Rankin L, Prentice D, Hagerman LA, Draenos C. Practice readiness of new nursing graduates: a concept analysis. Nurse Educ Pract. 2019;37:68–74. https://doi.org/10.1016/j.nepr.2019.04.009.
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8. https://doi.org/10.1038/nmeth.1226.
Mukherjee S, Berger MF, Jona G, Wang XS, Muzzey D, Snyder M, Young RA, Bulyk ML. Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarrays. Nat Genet. 2004;36(12):1331–9. https://doi.org/10.1038/ng1473.
Munekawa C, Okada H, Hamaguchi M, Habu M, Kurogi K, Murata H, Ito M, Fukui M. Fasting plasma glucose level in the range of 90–99 mg/dL and the risk of the onset of type 2 diabetes: Population-based Panasonic cohort study 2. J Diabetes Investig. 2022;13(3):453–9. https://doi.org/10.1111/jdi.13692.
Comments (0)