Biotechnology and Genetic Engineering using AI: A Review

Authors

  • K. Sreelakshmi Associate Professor, ECE Department, Ramireddy Subbarami Reddy Engineering College, Kadanuthala, Kavali, Nellore(Dt.), Andhra Pradesh, India-524142
  • Prabhat Kumar Vishwakarma Professor (CSE) and HOD(AI -DS), IIMT College of Engineering, Greater Noida,
  • S. Govind Rao Professor in CSE, Gokaraju Rangaraju Institute of Engineering and Technology(GRIET), Bachupally, Hyderabad-500090
  • Albia Maqbool Designation: Lecturer, Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Rafha, Kingdom of Saudi Arabia
  • Debashisa Samal assistant professor, Dept. of ECE, ITER, SOA Deemed to be University, Bhubaneswar.
  • Rashmi Saini Department of Computer Science and Engineering, G. B. Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, India.
  • Garima Thakur Chandigarh University, Punjab, India,

Keywords:

Artificial Intelligence, Biotechnology, Machine Learning, Genetic Engineering, Deep Learning, Neural Networks

Abstract

In this paper deep exploration into the intersection of Artificial Intelligence (AI), Biotechnology, and Genetic Engineering, three pioneering frontiers of modern science are presented. AI has been applied in Biotechnology and Genetic Engineering, accelerating research, improving precision, and expanding possibilities. The synergy of these interdisciplinary fields has resulted in emergent domains like Synthetic Biology and Systems Biology. The application of AI techniques, such as Machine Learning and Deep Learning, in tasks like biomarker discovery, drug discovery, gene editing, and genomics research are thoroughly discussed in this paper. Despite AI's potential, the paper also delves into the challenges that arise, including technical issues like overfitting, model interpretability, and the need for robust evaluation methodologies, as well as ethical and societal considerations. The critical role of mathematical and computational models in understanding and predicting complex biological systems is examined, spanning traditional models to state-of-the-art AI models. Detailed case studies provide practical examples of AI application in gene editing, drug discovery, metabolic engineering, synthetic biology, and personalized medicine. This paper shows that a reflection on the transformative potential of integrating AI, Biotechnology, and Genetic Engineering, underscoring the future research required in this rapidly evolving field and the potential benefits to society at large.

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Published

11.01.2024

How to Cite

Sreelakshmi, K. ., Vishwakarma, P. K. ., Rao, S. G. ., Maqbool, A. ., Samal, D. ., Saini, R. ., & Thakur, G. . (2024). Biotechnology and Genetic Engineering using AI: A Review. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 350–364. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4456

Issue

Vol. 12 No. 11s (2024)

Section

Research Article

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