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RR, Siva Kiran, P, Dhamodhar. (1404). The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review. سامانه مدیریت نشریات علمی, 80(4), 819-832. doi: 10.32592/ARI.2025.80.4.819
Siva Kiran RR; Dhamodhar P. "The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review". سامانه مدیریت نشریات علمی, 80, 4, 1404, 819-832. doi: 10.32592/ARI.2025.80.4.819
RR, Siva Kiran, P, Dhamodhar. (1404). 'The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review', سامانه مدیریت نشریات علمی, 80(4), pp. 819-832. doi: 10.32592/ARI.2025.80.4.819
RR, Siva Kiran, P, Dhamodhar. The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review. سامانه مدیریت نشریات علمی, 1404; 80(4): 819-832. doi: 10.32592/ARI.2025.80.4.819

The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review

Archives of Razi Institute
مقاله 3، دوره 80، شماره 4، مهر و آبان 2025، صفحه 819-832    اصل مقاله (678.24 K)
نوع مقاله: Review Article
شناسه دیجیتال (DOI): 10.32592/ARI.2025.80.4.819
نویسندگان
Siva Kiran RR* 1؛ Dhamodhar P2
1Department of Chemical Engineering, MS Ramaiah Institute of Technology, MSR Nagar, Mathikere, Bengaluru, India - 560054
2Department of Biotechnology, MS Ramaiah Institute of Technology, Bengaluru, India – 560054
چکیده
The integration of Machine Learning (ML) and Artificial Intelligence (AI) in animal biotechnology is revolutionizing the field, particularly in developing countries where agriculture and livestock play a significant role in the economy. AI and ML enable more efficient data analysis in areas such as genetic optimization, disease prediction, and livestock management, improving both productivity and sustainability. With the growing availability of data, AI-driven models can process large volumes of information from diverse sources like environmental conditions, genetic markers, and health records, offering more precise insights than traditional methods. Recent advancements include AI-powered diagnostic systems for detecting and managing disease outbreaks, which allow for faster response times and more targeted interventions, ultimately reducing economic losses. Enhanced breeding techniques now leverage machine learning algorithms to predict desirable genetic traits, enabling farmers to make data-informed breeding choices. Feed efficiency improvements, another critical area, benefit from AI's ability to analyze nutrient requirements and optimize feeding schedules based on individual animal needs, reducing waste and costs. Additionally, AI is increasingly applied in animal health monitoring, using tools such as sound-based systems and piezoelectric sensors embedded in smart collars that track behaviors indicative of health issues. In the dairy sector, AI models assess health risks like nitrate contamination in milk, contributing to safer food production and improving public health. In genetic studies, AI enhances selective breeding, improving traits like growth and disease resistance. This manuscript reviews the transformative role of AI and ML in animal biotechnology, focusing on developing regions, where resource optimization is crucial. By simplifying complex techniques and providing step-by-step tutorials, this work aims to equip researchers and practitioners with practical tools for harnessing AI in animal biotechnology.
کلیدواژه‌ها
Machine Learning؛ Animal Biotechnology؛ Forest Animals؛ Aquaculture؛ Simple AI Tutorials
عنوان مقاله [English]
نقش هوش مصنوعی و یادگیری ماشین در پیشبرد بیوتکنولوژی حیوانات: یک مرور
چکیده [English]
ادغام یادگیری ماشین (ML) و هوش مصنوعی (AI) در بیوتکنولوژی حیوانات در حال ایجاد تحولی اساسی در این حوزه است، به ویژه در کشورهای در حال توسعه که کشاورزی و دامداری نقش مهمی در اقتصاد دارند. هوش مصنوعی و یادگیری ماشین امکان تحلیل داده‌ها را در حوزه‌هایی مانند بهینه‌سازی ژنتیکی، پیش‌بینی بیماری و مدیریت دام‌ها به‌طور کارآمدتر فراهم می‌کنند و به بهبود بهره‌وری و پایداری منجر می‌شوند. با افزایش دسترسی به داده‌ها، مدل‌های مبتنی بر هوش مصنوعی می‌توانند حجم زیادی از اطلاعات را از منابع متنوعی مانند شرایط محیطی، نشانگرهای ژنتیکی و سوابق بهداشتی پردازش کرده و بینش‌های دقیق‌تری نسبت به روش‌های سنتی ارائه دهند. پیشرفت‌های اخیر شامل سیستم‌های تشخیصی مبتنی بر هوش مصنوعی برای شناسایی و مدیریت شیوع بیماری‌ها است که امکان پاسخ‌دهی سریع‌تر و مداخلات هدفمندتری را فراهم می‌کند و در نهایت زیان‌های اقتصادی را کاهش می‌دهد. تکنیک‌های پیشرفته پرورش اکنون از الگوریتم‌های یادگیری ماشین برای پیش‌بینی ویژگی‌های ژنتیکی مطلوب استفاده می‌کنند و به کشاورزان این امکان را می‌دهند تا تصمیمات پرورشی مبتنی بر داده بگیرند. بهبود کارایی تغذیه، که یک حوزه حیاتی دیگر است، از توانایی هوش مصنوعی در تحلیل نیازهای تغذیه‌ای و بهینه‌سازی زمانبندی تغذیه براساس نیازهای فردی حیوانات بهره می‌برد و ضایعات و هزینه‌ها را کاهش می‌دهد. علاوه بر این، هوش مصنوعی به‌طور فزاینده‌ای در پایش سلامت حیوانات با استفاده از ابزارهایی مانند سیستم‌های مبتنی بر صدا و حسگرهای پیزوالکتریک در قلاده‌های هوشمند که رفتارهای مرتبط با مشکلات سلامت را ردیابی می‌کنند، به کار گرفته می‌شود. در بخش لبنیات، مدل‌های هوش مصنوعی ریسک‌های بهداشتی مانند آلودگی نیترات در شیر را ارزیابی کرده و به تولید غذای ایمن‌تر و بهبود سلامت عمومی کمک می‌کنند. این مقاله نقش تحول‌آفرین هوش مصنوعی و یادگیری ماشین در بیوتکنولوژی حیوانات را بررسی می‌کند و بر مناطق در حال توسعه که بهینه‌سازی منابع در آن‌ها ضروری است، تمرکز دارد. با ساده‌سازی تکنیک‌های پیچیده و ارائه آموزش‌های گام به گام، این کار تلاش دارد تا محققان و متخصصان را به ابزارهای عملی برای استفاده از هوش مصنوعی در بیوتکنولوژی حیوانات مجهز کند.
کلیدواژه‌ها [English]
یادگیری ماشین, بیوتکنولوژی حیوانات, حیوانات جنگلی, آبزی‌پروری, آموزش‌های ساده هوش مصنوعی
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مراجع
  1. Chuluunsaikhan T, Kim J-H, Park S-H, Nasridinov A. Analyzing Internal and External Factors in Livestock Supply Forecasting Using Machine Learning: Sustainable Insights from South Korea. Sustainability. 2024;16(16):6907
  2. Martinho VJPD. Predictive machine learning models for livestock output. Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector: Springer; 2024. p. 35-47.
  3. Dehghan Z, Darya G, Mehdinejadiani S, Derakhshanfar A. Comparison of two methods of sperm‐and testis‐mediated gene transfer in production of transgenic animals: A systematic review. Animal Genetics. 2024;55(3):328-43
  4. Hoseinpoor E, Goudarztalejerdi A, Sazmand A. Molecular prevalence and phylogenetic analysis of hemotropic Mycoplasma species in cats in different regions of Iran. BMC microbiology. 2024;24(1):198
  5. Setiawan A, Utami E, editors. Predicting the Weight of Livestock Using Machine Learning. 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS); 2024: IEEE.
  6. Rozbahan B, Shams-Esfandabadi N, Kadivar A, Mokhtari A, Davoodian N. Prevalence of selected virulence factors of Staphylococcus aureus isolated in bovine mastitis in Chaharmahal and Bakhtiari province-Iran. Iranian Veterinary Journal. 2024;20(1):97-108
  7. Matboli M, Abdelbaky I, Khaled A, Khaled R, Hamady S, Farid LM, et al. Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model. Lipids in Health and Disease. 2024;23(1):266
  8. Dadar M, Bahreinipour A, Alamian S, Yousefi AR, Amiri K, Abnaroodheleh F. Serological, cultural, and molecular analysis of Brucella from Buffalo milk in various regions of Iran. Veterinary Research Communications. 2024;48(1):427-36
  9. Pedrosa VB, Chen S-Y, Gloria LS, Doucette JS, Boerman JP, Rosa GJ, et al. Machine learning methods for genomic prediction of cow behavioral traits measured by automatic milking systems in North American Holstein cattle. Journal of Dairy Science. 2024;107(7):4758-71
  10. Enferadi A, Ownagh A, Tavassoli M, editors. Molecular detection and phylogenetic analysis of Borrelia spp. from sheep and goats blood samples in West Azerbaijan province, Iran. Veterinary Research Forum; 2024.
  11. Li X, Chen X, Wang Q, Yang N, Sun C. Integrating bioinformatics and machine learning for genomic prediction in chickens. Genes. 2024;15(6):690
  12. Talebi R, Mardi M, Zeinalabedini M, Kazemi Alamouti M, Fabre S, Ghaffari MR. Assessing the performance of Moghani crossbred lambs derived from different mating systems with Texel and Booroola sheep. Plos one. 2024;19(4):e0301629
  13. Zhang H, Wu Y, Zhu Y, Ge L, Huang J, Qin Z. Identification and functional analysis of a serine protease inhibitor using machine learning strategy. International Journal of Biological Macromolecules. 2024;265:130852
  14. Seydi-Gazafi K, Tavassoli M, Mardani K. Investigation of pyrethroid resistance mutations in Linognathus stenopsis lice collected from goats in western and northwestern Iran. Frontiers in Veterinary Science. 2024;11:1380328
  15. Duraisamy N, Khan MY, Shah AU, Elalaoui RN, Cherkaoui M, Hemida MG. Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines. Frontiers in veterinary science. 2024;11:1468890
  16. Hajibarat Z, Saidi A, Zeinalabedini M, Mardi M, Ghaffari MR. Determining the Relationships Between Genotype and Phenotype Using Molecular Genetic Tools in Chickens. Jentashapir Journal of Cellular and Molecular Biology. 2024;15(1)
  17. Khan A, Zahid MA, Farrukh F, Abdelsalam SS, Mohammad A, Al-Zoubi RM, et al. Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis. International Immunopharmacology. 2024;141:112833
  18. Al-Aneed B, Masoudi AA, Katalani C, Ahmadian G, Hajizade A, Razmyar J. Evaluation of the expression of IFN-γ, IL-4, IL-17, and IL-22 cytokines in birds immunized with a Recombinant chimeric vaccine containing alpha toxin, netb, and ZMP against necrotic enteritis. Journal of Applied Biotechnology Reports. 2024;11(1):1229-35
  19. Yang L, Leynes C, Pawelka A, Lorenzo I, Chou A, Lee B, et al. Machine learning in time-lapse imaging to differentiate embryos from young vs old mice. Biology of Reproduction. 2024;110(6):1115-24
  20. Sardari M, Manouchehrifar M, Hasani K, Habibzadeh N, Doghaheh HP, Azimi T, et al. Molecular characterization and prevalence of β-lactamase-producing Enterobacterales in livestock and poultry slaughterhouses wastewater in Iran. Journal of Water and Health. 2024;22(3):572-83
  21. Shivaani M, Madan P. Application of imaging and spectroscopy techniques for grading of bovine embryos-a review. Frontiers in Veterinary Science. 2024;11:1364570
  22. Sarani S, Enferadi A, Hasani SJ, Sarani MY, Rahnama M, Sarani F. Identification of zoonotic pathogenic bacteria from blood and ticks obtained from hares and long-eared hedgehogs (Hemiechinus megalofis) in eastern Iran. Comparative Immunology, Microbiology and Infectious Diseases. 2024;104:102097
  23. Saiki N, Adachi A, Ohnishi H, Koga A, Ueki M, Kohno K, et al. Development of an AI-Assisted Embryo Selection System Using Iberian Ribbed Newts for Embryo–Fetal Development Toxicity Testing. Yonago Acta Medica. 2024;67(3):233-41
  24. Manshadi ED, Rezvani ME, Hassanpour A, Mehrgerdi FZ, Lotfi M. Reduction of Apoptotic Gene Expression by Platelet-rich Plasma in a Mouse Model of Premature Ovarian Failure. Biomedical Research and Therapy. 2024;11(5):6447-56
  25. Luo Z, Yu Y, Bao Z, Li F. Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp. Aquaculture. 2024;581:740376
  26. Azizi-Dargahlou S, Pouresmaeil M, Ahmadabadi M. Tobacco plant: a novel and promising heterologous bioreactor for the production of recombinant bovine chymosin. Molecular Biotechnology. 2024;66(9):2595-605
  27. Liyanage D, Lee S, Yang H, Lim C, Omeka W, Sandamalika WG, et al. Genomic prediction of survival traits in the response of olive flounder (Paralichthys olivaceus) to viral hemorrhagic septicemia virus: Comparing machine learning models and traditional approaches. Aquaculture. 2025;595:741685
  28. Zamani P, Mashreghi M, Bazaz MR, Zargari S, Alizadeh F, Dorrigiv M, et al. Characterization of stability, safety and immunogenicity of the mRNA lipid nanoparticle vaccine Iribovax® against COVID-19 in nonhuman primates. Journal of Controlled Release. 2023;360:316-34
  29. Adebayo OM, Popoola MA, Kuusu DJ, Fanwo RR, Shoyombo AJ, Ndiomu EP, et al., editors. Application of bioinformatics in animal breeding and genetics: A review. 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG); 2024: IEEE.
  30. Sotoudeh M, Alavi N, Zarrineh A. Improving Online Livestock Health Monitoring System using Machine Learning, AI, ANN, IoT and Sound_based Technologies: A Pilot Study. 2024
  31. Shirzadifar A, Manafiazar G, Davoudi P, Do D, Hu G, Miar Y. Prediction of growth and feed efficiency in mink using machine learning algorithms. animal. 2025;19(2):101330
  32. Mohammadpour A, Samaei MR, Baghapour MA, Alipour H, Isazadeh S, Azhdarpoor A, et al. Nitrate concentrations and health risks in cow milk from Iran: insights from deterministic, probabilistic, and AI modeling. Environmental Pollution. 2024;341:122901
  33. Gharibshahian M, Torkashvand M, Bavisi M, Aldaghi N, Alizadeh A. Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine. Skin Research and Technology. 2024;30(9):e70016
  34. Das S, Roy RK, Bezboruah T. Machine learning in animal healthcare: A comprehensive review. International Journal of Recent Engineering Science. 2024;11(3):89-93
  35. Ghaderzadeh M, Shalchian A, Irajian G, Sadeghsalehi H, Sabet B. Artificial intelligence in drug discovery and development against antimicrobial resistance: A narrative review. Iranian Journal of Medical Microbiology. 2024;18(3):135-47
  36. Xie G, Bai X, Peng Y, Li Y, Zhang C, Liu Y, et al. Aquaculture ponds identification based on multi-feature combination strategy and machine learning from Landsat-5/8 in a typical inland lake of China. Remote Sensing. 2024;16(12):2168
  37. Nazari Ashani M, Alesheikh AA, Lotfata A. Nationwide Spatiotemporal prediction of foot and mouth disease in Iran using machine learning (2008–2018). Spatial Information Research. 2024;32(6):775-86
  38. Ahmed MS, Jeba SM. SalmonScan: A novel image dataset for machine learning and deep learning analysis in fish disease detection in aquaculture. Data in Brief. 2024;54:110388
  39. Bani Saadat H, Vaez Torshizi R, Manafiazar G, Masoudi AA, Ehsani A, Shahinfar S. Comparing machine learning algorithms and linear model for detecting significant SNPs for genomic evaluation of growth traits in F2 chickens. Journal of Agricultural Science and Technology. 2024;26(6):1261-74
  40. Bakhit AA, Sabli N, Jamlos M, Jamlos M, Ramli N, Nordin M, et al. IoT-Based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster. Journal of Advanced Research in Applied Mechanics. 2024;117(1):137-49
  41. Jantz P, Abraham A, Scheffers B, Gaillard C, Harfoot M, Goetz S, et al. Functional traits and phylogeny predict vertical foraging in terrestrial mammals and birds. bioRxiv. 2024:2024.04. 18.589860
  42. Meshgi B, Hanafi-Bojd AA, Fathi S, Modabbernia G, Meshgi K, Shadman M. Multi-scale habitat modeling framework for predicting the potential distribution of sheep gastrointestinal nematodes across Iran’s three distinct climatic zones: a MaxEnt machine-learning algorithm. Scientific Reports. 2024;14(1):2828
  43. Zhang G, Xie H, Dai X. DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes. International Journal of Molecular Sciences. 2024;25(20):10928
  44. Hossein-Zadeh NG. An overview of recent technological developments in bovine genomics. Veterinary and Animal Science. 2024;25:100382
  45. Bennett KL, McMillan WO, Loaiza JR. The genomic signal of local environmental adaptation in Aedes aegypti mosquitoes. Evolutionary Applications. 2021;14(5):1301-13
  46. Kueneman JG, Esser HJ, Weiss SJ, Jansen PA, Foley JE. Tick microbiomes in neotropical forest fragments are best explained by tick-associated and environmental factors rather than host blood source. Applied and environmental microbiology. 2021;87(7):e02668-20
  47. Mason DM, Friedensohn S, Weber CR, Jordi C, Wagner B, Meng S, et al. Deep learning enables therapeutic antibody optimization in mammalian cells by deciphering high-dimensional protein sequence space. BioRxiv. 2019:617860
  48. Wang G, Shin S-Y, Shin K-W, Lee H-C, editors. An Improved Deep Learning Method for Animal Images. Proceedings of the Korean Society of Computer Information Conference; 2019: Korean Society of Computer Information.
  49. Mihaescu MC, Popescu PS. Review on publicly available datasets for educational data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2021;11(3):e1403
  50. RR SK. The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review. Archives of Razi Institute. 2025
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