Современные тенденции применения искусственного интеллекта в области биотехнологии США
Аннотация и ключевые слова
Аннотация (русский):
Применение искусственного интеллекта (ИИ) в сфере биотехнологий США подразумевает цифровизацию процессов в сельском хозяйстве: растениеводстве и животноводстве. На основе полученного массива данных технологии машинного обучения позволяют исследовать ключевые биологические процессы, контролировать и управлять ими. Системы ИИ интегрируют с другими цифровыми технологиями, такими как датчики процессов и состояний, киберфизические системы, беспилотные летательные аппараты, которые в совокупности с алгоритмами компьютерного зрения и глубокого обучения помогают контролировать состояние сельскохозяйственных культур и почвы, отслеживать и прогнозировать изменения окружающей среды, влияющие на урожайность сельскохозяйственных культур. «Умное» сельское хозяйство позволяет оценить экологическую устойчивость через круговорот питательных веществ и экономическую стабильность благодаря управлению пахотными и пастбищными угодьями при помощи сенсорных систем, фиксирующих данные о почве, растениях и погоде. Цифровая трансформация и применение искусственного интеллекта является перспективным инновационным направлением, которое обладает огромным потенциалом для повышения эффективности, точности и скорости исследований и разработок, а также создает новые условия для появления революционно новых продуктов и услуг.

Ключевые слова:
искусственный интеллект, продовольственная безопасность, биоразнообразие, биотехнология сельскохозяйственных культур
Список литературы

1. David L, Thakkar A, Mercado R, Engkvist O. Molecular representations in AI-driven drug discovery: a review and practical guide. J Chemin- 2020;12(1):1–22. https:// doi.org/10.1186/s13321-020-00460-5.

2. Diaw MD, Papelier S, Durand-Salmon A, Felblinger J, Oster J. AI-assisted QT measurements for highly automated drug safety studies. IEEE Trans Biomed Eng 2022. https://doi.org/10.1109/TBME.2022.3221339.

3. van der Lee M, Swen JJ. Artificial intelligence in pharmacology research and practice. ClinTranslSci 2023;16(1):31–6. https://doi.org/10.1111/cts.13431.

4. Roche-Lima A, Roman-Santiago A, Feliu-Maldonado R, et al. Machine learning algorithm for predicting warfarin dose in caribbeanhispanics using pharmacogenetic data. Front Pharmacol 2020;10:1550. https://doi.org/10.3389/ fphar.2019.01550.

5. Lin E, Lin C-H, Lane H-Y. Precision psychiatry applications with pharmacogenomics: artificial intelligence and machine learning approaches. Int J MolSci 2020;21(3):969. https://doi.org/10.3390/ijms21030969.

6. Caudai C, Galizia A, Geraci F, et al. AI applications in functional genomics. ComputStructBiotechnol J 2021;19:5762–90. https://doi.org/10.1016/j. csbj.2021.10.009.

7. Lin J, Ngiam KY. How data science and AI-based technologies impact genomics. Singap Med J 2023;64(1):59–66. https://doi.org/10.4103/singaporemedj.SMJ-2021-438.

8. Xiao Q, Zhang F, Xu L, et al. High-throughput proteomics and AI for cancer biomarker discovery. Adv Drug Deliv Rev 2021;176:113844. https://doi.org/https://doi.org/10.1016/j.addr.2021.113844.

9. Mund A, Coscia F, Hollandi R, et al. AI-driven Deep Visual Proteomics defines cell identity and heterogeneity. BioRxiv 2021. https://doi.org/10.1101/2021.01.25.427969.

10. Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications. Cell Rep Phys Sci 2022;3:7. https://doi. org/10.1016/j.xcrp.2022.100978.

11. Oliveira AL. Biotechnology, big data and artificial intelligence. Biotechnol J 2019; 14(8):1800613. https://doi.org/10.1002/biot.201800613.

12. Goh WWB, Sze CC. AI paradigms for teaching biotechnology. Trends Biotechnol 2019;37(1):1–5. https://doi.org/10.1016/j.tibtech.2018.09.009.

13. Kim H. AI, big data, and robots for the evolution of biotechnology. Genom Inform 2019;17(4):e44. https://doi.org/10.5808/GI.2019.17.4.e44.

14. Turing AM. Computing machinery and intelligence. Mind 1950;59(236):433–60. https://doi.org/10.1093/mind/LIX.236.433.

15. Holzinger A, Kickmeier-Rust M, Müller H. Kandinsky patterns as IQ-test for machine learning. Lecture Notes in Computer Science LNCS 11713. Cham: Springer/Nature; 2019. p. 1–14. https://doi.org/10.1007/978-3-030-29726-8-1.

16. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag 2006; 27(4):12–4. https://doi.org/10.1609/aimag.v27i4.1904.

17. Bratko I, Muggleton S. Applications of inductive logic programming. Commun ACM 1995;38(11):65–70. https://doi.org/10.1145/219717.219771.

18. Muggleton SH, Schmid U, Zeller C, Tamaddoni-Nezhad A, Besold T. Ultra-strong machine learning: comprehensibility of programs learned with ILP. Mach Learn 2018;107:1119–40. https://doi.org/10.1007/s10994-018-5707-3.

19. Russell S.J., Norvig P. Artificial intelligence: a modern approach (4th edition). Upper Saddle River: Prentice Hall; 2020.

20. Hendler J. Avoiding another AI winter. IEEE IntellSyst 2008;23(2):2–4. https:// doi.org/10.1109/MIS.2008.20.

21. King MR. The future of AI in medicine: a perspective from a chatbot. Ann Biomed Eng 2022:1–5. https://doi.org/10.1007/s10439-022-03121-w.

22. Müller H, Holzinger A, Plass M, Brcic L, Stumptner C, Zatloukal K. Explainability and causability for artificial intelligence-supported medical image analysis in the context of the european in vitro diagnostic regulation. N Biotechnol 2022;70: 67–72. https://doi.org/10.1016/j.nbt.2022.05.002.

23. Holzinger A, Saranti A, Angerschmid A, et al. Digital transformation in smart farm and forest operations needs human-centered ai: challenges and future directions. Sensors 2022;22(8):3043. https://doi.org/10.3390/s22083043.

24. Naqvi RZ, Siddiqui HA, Mahmood MA, et al. Smart breeding approaches in post- genomics era for developing climate-resilient food crops. Front Plant Sci 2022:13. https://doi.org/10.3389/fpls.2022.972164.

25. Barnabas B, J´ ager K, Feh¨ er A. The effect of drought and heat stress on reproductive ´ processes in cereals. Plant, Cell Environ 2008;31(1):11–38. https://doi.org/https://doi.org/10.1111/j.1365-3040.2007.01727.x.

26. Barnes ML, Breshears DD, Law DJ, et al. Beyond greenness: detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data. PLoS One 2017;12(12):e0189539. https://doi.org/10.1371/journal.pone.0189539.

27. Holzinger A, Weippl E, Tjoa AM, Kieseberg P. Digital transformation for sustainable development goals (SDGs) - a security, safety and privacy perspective on AI. Springer Lecture Notes in Computer Science, LNCS 12844. Cham: Springer; 2021. https://doi.org/10.1007/978-3-030-84060-0_1.

28. Fiorani F, Schurr U. Future scenarios for plant phenotyping. Annu Rev Plant Biol 2013;64:267–91. https://doi.org/10.1146/annurev-arplant-050312-120137.

29. Tester M, Langridge P. Breeding technologies to increase crop production in a changing world. Science 2010;327(5967):818–22. https://doi.org/10.1126/ science.1183700.

30. Holzinger A, Haibe-Kains B, Jurisica I. Why imaging data alone is not enough: AI- based integration of imaging, omics, and clinical data. Eur J Nucl Med Mol Imaging 2019;46(13):2722–30. https://doi.org/10.1007/s00259-019-04382-9.

31. Rouphael Y, Spíchal L, Panzarova K, Casa R, Colla G. High-throughput plant ´ phenotyping for developing novel biostimulants: from lab to field or from field to lab. Front Plant Sci 2018;9:1197. https://doi.org/10.3389/fpls.2018.01197.

32. Rico-Chavez AK, Franco JA, Fernandez-Jaramillo AA, Contreras-Medina LM, ´ Guevara-Gonzalez RG, Hernandez-Escobedo Q. Machine learning for plant stress ´ modeling: a perspective towards hormesis management. Plants 2022;11(7):970. https://doi.org/10.3390/plants11070970.

33. Jung J, Maeda M, Chang A, Bhandari M, Ashapure A, Landivar-Bowles J. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. CurrOpinBiotechnol 2021;70:15–22. https://doi.org/10.1016/j.copbio.2020.09.003.

34. Zhu Y, Cao Z, Lu H, Li Y, Xiao Y. In-field automatic observation of wheat heading stage using computer vision. BiosystEng 2016;143:28–41. https://doi.org/https://doi.org/10.1016/j.biosystemseng.2015.12.015.

35. Deng L, Du H, Han Z. A carrot sorting system using machine vision technique. ApplEngAgric 2017;33(2):149–56. https://doi.org/10.13031/aea.11549.

36. Iraji MS. Comparison between soft computing methods for tomato quality grading using machine vision. J Food MeasCharact 2019;13(1):1–15. https://doi.org/10.1007/s11694-018-9913-2.

37. Ribaut J, De Vicente M, Delannay X. Molecular breeding in developing countries: challenges and perspectives. CurrOpin Plant Biol 2010;13(2):213–8. https://doi. org/10.1016/j.pbi.2009.12.011.

38. Hesami M, Jones AMP. Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture. ApplMicrobiolBiotechnol 2020;104:9449–85. https://doi.org/10.1007/s00253-020-10888-2.

39. Lal R. Soil carbon sequestration to mitigate climate change. Geoderma 2004;123 (1–2):1–22. https://doi.org/10.1016/j.geoderma.2004.01.032.

40. Wilhelm RC, van Es HM, Buckley DH. Predicting measures of soil health using the microbiome and supervised machine learning. Soil BiolBiochem 2022;164: 108472. https://doi.org/10.1016/j.soilbio.2021.108472.

41. de Andrade VHGZ, Redmile-Gordon M, Barbosa BHG, Andreote FD, Roesch LFW, Pylro VS. Artificially intelligent soil quality and health indices for ‘next generation’food production systems. Trends Food SciTechnol 2021;107:195–200. https://doi.org/10.1016/j.tifs.2020.10.018.

42. Marselle MR, Hartig T, Cox DT, et al. Pathways linking biodiversity to human health: a conceptual framework. Environ Int 2021;150:106420. https://doi.org/https://doi.org/10.1016/j.envint.2021.106420.

43. Blum WE, Zechmeister-Boltenstern S, Keiblinger KM. Does soil contribute to the human gut microbiome? Microorganisms 2019;7(9):287. https://doi.org/10.3390/microorganisms7090287.

44. Mueller H, Mayrhofer MT, Veen E-BV, Holzinger A. The ten commandments of ethical medical AI. IEEE COMPUTER 2021;54(7):119–23. https://doi.org/https://doi.org/10.1109/MC.2021.3074263.

45. Angerschmid A, Zhou J, Theuermann K, Chen F, Holzinger A. Fairness and explanation in AI-informed decision making. Mach Learn KnowlExtr 2022;4(2): 556–79. https://doi.org/10.3390/make4020026.

46. Holzinger K, Mak K, Kieseberg P, Holzinger A. Can we trust machine learning results? Artificialintelligenceinsafety-criticaldecisionsupport. ERCIM N 2018; 112(1):42–3.

47. Holzinger A. The next frontier: AI we can really trust. In: Kamp M, editor. Proceedings of the ECML PKDD 2021, CCIS 1524. Cham: Springer Nature; 2021. https://doi.org/10.1007/978-3-030-93736-2_33.

48. Holzinger A, Dehmer M, Emmert-Streib F, et al. Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Inf Fusion 2022;79(3):263–78. https://doi.org/https://doi.org/10.1016/j.inffus.2021.10.007.

49. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 2019;1(5):206–15. https://doi.org/10.1038/s42256-019-0048-x.

50. Holzinger A, Saranti A, Molnar C, Biececk P, Samek W. Explainable AI methods - a brief overview. XXAI - Lecture Notes in Artificial Intelligence LNAI 13200. Cham: Springer; 2022. https://doi.org/10.1007/978-3-031-04083-2_2.

51. Müller H, Reihs R, Zatloukal K, Holzinger A. Analysis of biomedical data with multilevel glyphs. BMC Bioinforma 2014;15(Suppl 6). https://doi.org/10.1186/1471-2105-15-S6-S5.

52. Hund M, Boehm D, Sturm W, et al. Visual analytics for concept exploration in subspaces of patient groups: making sense of complex datasets with the doctor-in- the-loop. Brain Inform 2016;3(4):233–47. https://doi.org/10.1007/s40708-016- 0043-5.

53. Roy Edwards. A new ZeroBounce study reveals countries advancing AI the fastest. https://www.enterprisetimes.co.uk/2024/08/06/a-new-zerobounce-study-reveals-countries-advancing-ai-the-fastest/

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