نقش هوش مصنوعی در ژنومیکس

نوع مقاله : مقاله پژوهشی

نویسندگان

1 نویسنده مسئول: استاد بخش علوم دامی، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، ایران

2 استادیار، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران

3 استادیار، دانشگاه ملی علوم محیطی و زیستی اوکراین، اوکراین

4 استادیار، گروه علوم دامی، دانشگاه ملی کشاورزی بیلا تسرکوا، بیلا تسرکوا، اوکراین

5 استادیار، گروه علوم دامی، دانشگاه ملی کشاورزی بیلا تسرکوا، بیلا تسرکوا، اوکراین.

6 دانشگاه ملی کشاورزی سومی، سومی، اوکراین

7 دانشیار، گروه فناوری‌های تولید و فرآوری دام، دانشگاه دولتی پودیلیا، اوکراین

8 دانشیار، گروه فناوری های تولید و فرآوری دام، دانشگاه دولتی پودیلیا، اوکراین

چکیده

هدف: تولید داده‌ در زیست‌شناسی و زیست‌فناوری در سال‌های گذشته به دلیل توسعه بسیار سریع فناوری‌های با کارایی بالا بسیار زیاد شده است. این داده‌ها با مطالعه مولکول‌های زیستی، از قبیل متابولیت‌ها، پروتئین‌ها، RNA و DNA برای درک و فهم نقش این مولکول‌ها در تعیین ساختار، عملکرد و دینامیک سیستم‌های زنده حاصل شده‌اند. علاوه بر این، در یک برنامه اصلاح نژادی، پیشرفت ژنتیکی را می‌توان از طریق شناسایی دقیق حیوانات برتر که به عنوان والدین نسل بعدی انتخاب می شوند، به حداکثر رساند و در نتیجه به اهداف اصلاح نژادی دست یافت. شبکه‌های عصبی مصنوعی برای کاهش این محدودیت روش‌های رگرسیون سنتی پیشنهاد شده‌اند و می‌توانند برای مدیریت داده‌های غیرخطی و پیچیده، حتی زمانی که داده‌ها نادقیق و نویز هستند، استفاده شوند. داده‌های اومیکس می‌توانند به قدری بیش از حد بزرگ و پیچیده باشند که از طریق تجزیه و تحلیل بصری یا همبستگی‌های آماری قابل بررسی نیستند. این امر استفاده از هوش ماشینی یا هوش مصنوعی را تشویق کرده است. اهداف این مطالعه عبارتند از بررسی کاربردهای اصلی روش‌های هوش مصنوعی در ژنومیکس عملکردی، سرطان، کشاورزی، حیوانات اهلی و زمینه‌های درهم تنیده آن یعنی اپی‌ژنومیکس، ترانس‌کریپتومیکس، اپی‌ترانس‌کریپتومیکس، پروتئومیکس و متابولومیکس، بحث در مورد جنبه‌های مهم مدیریت داده‌ها، مانند یکپارچه‌سازی داده‌ها، جانهی، تمیز کردن، حذف نویز، متعادل‌سازی و نسبت داده‌های از دست رفته، مدل‌سازی سیستم-ژنومیکس عملکردی، هوش مصنوعی و سیستم‌های بیولوژی، پرداختن به مسائل حقوقی، اخلاقی و اقتصادی مربوط به کاربرد روش‌های هوش مصنوعی در حوزه ژنومیکس و ارائه نمایی از سناریوهای احتمالی آینده است.
مواد و روش‌ها: در این بررسی سعی شد کلیه پژوهش‌های انجام شده در زمینه کاربرد هوش مصنوعی در ژنومیکس عملکردی، سرطان، کشاورزی، حیوانات اهلی و زمینه‌های درهم تنیده آن یعنی اپی‌ژنومیکس، ترانس‌کریپتومیکس، اپی‌ترانس‌کریپتومیکس، پروتئومیکس و متابولومیکس، با تمرکز بر روی کاربردهای سال های اخیر پس از افزایش تولید کلان داده مطالعه و مورد استفاده قرار گیرند.
نتایج: بررسی‌ها نشان داد که کاربرد هوش مصنوعی در همه رشته‌ها از چمله ژنومیکس عملکردی، سرطان، کشاورزی، حیوانات اهلی و زمینه‌های درهم تنیده آن یعنی اپی‌ژنومیکس، ترانس‌کریپتومیکس، اپی‌ترانس‌کریپتومیکس، پروتئومیکس و متابولومیکس با سرعت زیادی رو به افزایش است و فواید زیادی دارد.
نتیجه‌گیری: با توجه به کاربردهای حیاتی که اغلب توسط زیست شناسی و به ویژه ژنومیک عملکردی به آن پرداخته می شود، بهتر است با ابزارهای هوش مصنوعی که قادر به کمک به درک مکانیکی فرآیندهای بیولوژیکی هستند، سروکار داشته باشیم.

کلیدواژه‌ها


عنوان مقاله [English]

The role of artificial intelligence in genomics

نویسندگان [English]

  • Mohammadreza Mohammadabadi 1
  • Hamid Kheyrodin 2
  • Volodymyr Afanasenko 3
  • Olena Babenko Ivanivna 4
  • Nataliia Klopenko 5
  • Oleksandr Kalashnyk 6
  • Yulia Ievstafiieva 7
  • Vita Buchkovska 8
1 Corresponding Author. Professor, Animal Science Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2 Assistant Professor, Semnan University, Semnan, Iran
3 National University of Life and Environmental Sciences of Ukraine, Ukraine.
4 Assistant Professor, Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine
5 Assistant Professor, Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine.
6 Sumy National Agrarian University, Sumy, Ukraine.
7 Associate professor, Department of Technologies of Livestock Production and processing, Higher Educational Institution “Podillia State University”, Ukraine
8 Associate Professor, Department of Technologies of Livestock Production and processing, Higher Educational Institution “Podillia State University”, Ukraine
چکیده [English]

Objective
Data generation in biology and biotechnology has greatly increased in recent years due to the very rapid development of high-performance technologies. These data are obtained by studying biological molecules, such as metabolites, proteins, RNA, and DNA, to understand the role of these molecules in determining the structure, function, and dynamics of living systems. Functional genomics is a field of research that aims to characterize the function and interaction of all the major components (DNA, RNA, proteins, and metabolites, along with their modifications) that contribute to the set of observable characteristics of a cell or individual (i.e., phenotype). Furthermore, in a breeding program, genetic improvement can be maximized through accurate identification of superior animals that are selected as parents of the next generation, thereby achieving breeding goals. Artificial neural networks have been proposed to alleviate this limitation of traditional regression methods and can be used to handle nonlinear and complex data, even when the data is imprecise and noisy. Omics data can be too large and complex to handle through visual analysis or statistical correlations. This has encouraged the use of machine intelligence or artificial intelligence. The objectives of this study was to review the main applications of artificial intelligence methods in functional genomics, cancer, agriculture, domestic animals and its intertwined fields, i.e. epigenomics, transcriptomics, epitranscriptomics, proteomics and metabolomics, discuss important aspects of data management, such as data integration, , cleaning, noise removal, balancing and ratio of missing data, functional genomics-system modeling, artificial intelligence and systems biology, addressing legal, ethical and economic issues related to the application of artificial intelligence methods in the field of genomics and presenting a view of possible scenarios in the future.

Materials and methods
In this review, all the researches conducted in the field of artificial intelligence application in functional genomics, cancer, agriculture, domestic animals and its intertwined fields, i.e. epigenomics, transcriptomics, epitranscriptomics, proteomics and metabolomics, were tried, focusing on the applications of recent years after Increase production of big data to be studied and used.

Results
The studies showed that the application of artificial intelligence in all fields, including functional genomics, cancer, agriculture, domestic animals and its intertwined fields, i.e., epigenomics, transcriptomics, epitranscriptomics, proteomics, and metabolomics, is increasing rapidly and has many benefits.

کلیدواژه‌ها [English]

  • Artificial intelligence
  • Genomics
  • Agriculture
  • Domestic animals
  • Cancer
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