Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T
Machine learning and Deep learning applications in CAR-T research and development.
Sección
Editorial
Cómo citar
Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T.
Rev. colomb. hematol. oncol. [Internet]. 2024 Mar. 10 [cited 2024 Dec. 21];10(2):7-11. Disponible en: https://doi.org/10.51643/22562915.671
Dimensions
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Mostrar biografía de los autores
Visitas del artículo 161 | Visitas PDF 157
- Garcia JM, Burnett CE, Roybal KT. Toward the clinical development of synthetic immunity to cancer [Internet]. Immunological Reviews. John Wiley and Sons Inc; 2023. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/imr.13245
- Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature [Internet]. 2017 Oct 18;550(7676):354–9. Available from: https://www.nature.com/articles/nature24270
- Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language Models are Unsupervised Multitask Learners [Internet]. Available from: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
- Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language Models are Few-Shot Learners. 2020 May 28 [cited 2023 Nov 7]; Available from: https://arxiv.org/abs/2005.14165v4
- He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. 2017 Mar 20; Available from: http://arxiv.org/abs/1703.06870
- Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature [Internet]. 2021 Aug 26;596(7873):583–9. Available from: https://www.nature.com/articles/s41586-021-03819-2#citeas
- Choudhry P, Gugliemini O, Geng H, Sarin V, Sarah L, Paranjape N, et al. Functional multi-omics reveals genetic and pharmacologic regulation of surface CD38 in multiple myeloma. Available from: https://doi.org/10.1101/2021.08.04.455165
- Hie BL, Shanker VR, Xu D, Bruun TUJ, Weidenbacher PA, Tang S, et al. Efficient evolution of human antibodies from general protein language models. Nat Biotechnol [Internet]. 2023; Available from: https://www.nature.com/articles/s41587-023-01763-2
- Naghizadeh A, Tsao WC, Cho JH, Xu H, Mohamed M, Li D, et al. In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes. PLoS Comput Biol [Internet]. 2022 Mar 1;18(3). Available from: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009883
- Lee M, Lee YH, Song J, Kim G, Jo YJ, Min HS, et al. Deep-learning based three-dimensional 1 label-free tracking and analysis of immunological synapses of car-t cells. Elife [Internet]. 2020 Dec 1;9:1–53. Available from: https://elifesciences.org/articles/49023
- Dannenfelser R, Allen GM, VanderSluis B, Koegel AK, Levinson S, Stark SR, et al. Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies. Cell Syst [Internet]. 2020 Sep 23;11(3):215-228.e5. Available from: https://www.sciencedirect.com/science/article/pii/S2405471220302866
- Patiño-Escobar B, Talbot A, Wiita AP. Overcoming proteasome inhibitor resistance in the immunotherapy era. Trends Pharmacol Sci [Internet]. 2023 Aug 1;44(8):507–18. Available from: https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(23)00111-6
- Ferguson ID, Patiño-Escobar B, Tuomivaara ST, Lin YHT, Nix MA, Leung KK, et al. The surfaceome of multiple myeloma cells suggests potential immunotherapeutic strategies and protein markers of drug resistance. Nat Commun [Internet]. 2022 Dec 1;13(1). Available from: https://www.nature.com/articles/s41467-022-31810-6
- Patino-Escobar B, Kasap C, Ferguson I, Hale M, Wiita A. P-103: Profiling the myeloma cell surface proteome reveals CCR10 as a potential immunotherapeutic target. Clin Lymphoma Myeloma Leuk [Internet]. 2021 Oct;21:S94–5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2152265021022370
- Long AH, Haso WM, Shern JF, Wanhainen KM, Murgai M, Ingaramo M, et al. 4-1BB costimulation ameliorates T cell exhaustion induced by tonic signaling of chimeric antigen receptors. Nat Med [Internet]. 2015 Jun 9;21(6):581–90. Available from: https://www.nature.com/articles/nm.3838
- Boucher JC, Li G, Kotani H, Cabral ML, Morrissey D, Lee SB, et al. CD28 costimulatory domain-targeted mutations enhance chimeric antigen receptor T-cell function. Cancer Immunol Res [Internet]. 2021 Jan 1;9(1):62–74. Available from: https://aacrjournals.org/cancerimmunolres/article/9/1/62/470226/CD28-Costimulatory-Domain-Targeted-Mutations
- Gattinoni L, Speiser DE, Lichterfeld M, Bonini C. T memory stem cells in health and disease [Internet]. Vol. 23, Nature Medicine. Nature Publishing Group; 2017. p. 18–27. Available from: https://www.nature.com/articles/nm.4241
- Singh N, Perazzelli J, Grupp SA, Barrett DM. Early memory phenotypes drive T cell proliferation in patients with pediatric malignancies [Internet]. Available from: https://www.science.org/doi/10.1126/scitranslmed.aad5222
- Daniels KG, Wang S, Simic MS, Bhargava HK, Capponi S, Tonai Y, et al. Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning [Internet]. Available from: https://www.science.org/doi/abs/10.1126/science.abq0225?af=R&utm_source=sfmc&utm_medium=email&utm_campaign=1stRelease&utm_content=alert&et_rid=719334783&et_cid=4522378