Название: Artificial Intelligence in Biotechnology: Biomanufacturing and Precision Medicine
Автор: Anil K. Sharma, Poonam Bansal, Priti Duhan
Издательство: De Gruyter
Год: 2026
Страниц: 556
Язык: английский
Формат: pdf, epub
Размер: 12.3 MB

How is AI shaping advancements in genomics, precision medicine, proteomics, metabolomics, drug discovery and development, biomedical imaging, synthetic biology, smart agriculture, production processes for biologics, vaccines and biofuels, computational biology and environmental biotechnology? Artificial Intelligence (AI) offers great potential in biotechnology research, discovery and innovation. This book discusses how AI is enabling deeper insights into biological systems and unlocking new possibilities for innovation. It addresses the ethical, legal and regulatory challenges arising from the integration of AI in biotechnology. It gives an overview of innovative tools such as Deep Learning frameworks, neural networks and AI-integrated lab automation systems and presents case studies showcasing successful collaborations between AI and biotechnology across industries.

The convergence of biotechnology and Artificial Intelligence (AI) heralds a transformative era in scientific innovation. Biotechnology with its profound ability to manipulate biological systems for the betterment of humanity and AI, with its unparalleled capacity to process, analyze, and predict patterns in vast datasets, is revolutionizing our understanding and interaction with the living world. This book explores this dynamic intersection, offering a comprehensive examination of the tools, applications, and future potential of these integrated disciplines. The primary objective of this book is to illuminate how AI is reshaping biotechnology across diverse domains, including genomics, proteomics, healthcare, agriculture, and environmental sustainability. By harnessing AI, researchers and industry professionals are tackling some of the most pressing challenges in biotechnology – from accelerating drug discovery and optimizing production processes to personalizing medical treatments and addressing global environmental issues.

The future of biotechnology in the AI era holds extraordinary promise. As AI technologies become more sophisticated, their integration into biotechnology will catalyze unprecedented advancements. The advent of quantum computing is poised to further enhance AI’s capabilities, enabling breakthroughs in complex biological problems such as protein folding and metabolic pathway optimization. AI-driven insights into genetic and phenotypic data will pave the way for ultra-personalized healthcare, where treatments are tailored to individual patients with unparalleled precision. Additionally, AI will play a pivotal role in addressing global challenges like climate change and food security by optimizing biomanufacturing processes and creating sustainable agricultural practices. These advancements will necessitate interdisciplinary collaborations across computer science, biology, chemistry, and ethics, fostering innovation while addressing complex societal challenges.

Machine Learning (ML) is a subset of AI that enables computers to learn from data without explicit programming. In biotechnology, ML models are widely employed to classify biological samples, predict disease outcomes, and model genetic interactions. Algorithms such as support vector machines, random forests, and k-nearest neighbors have shown success in bioinformatics applications, particularly where feature selection and model interpretability are important. DL, a specialized form of ML, uses neural networks with multiple layers (deep neural networks) to model highly nonlinear relationships. Convolutional neural networks (CNNs) have become instrumental in biomedical imaging tasks, such as identifying cancerous cells or segmenting organs in radiological scans. Recurrent neural networks, and more recently, transformers, are increasingly applied to sequential data like gene expression time series and protein sequences. Artificial neural networks, the backbone of DL, are inspired by the structure of the human brain but operate through mathematical abstraction. In biotechnology, neural networks are being integrated into workflows for predicting protein folding, simulating metabolic pathways, and modeling cell signaling dynamics.

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