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Pharma Tech Outlook | Saturday, March 26, 2022
AI has the potential to revolutionize the field of biotechnology. Companies in the biotech industry can use artificial intelligence (AI) in a variety of ways to improve their workflows and innovate.
Fremont, CA: Biotechnology firms are discovering the value that AI can bring to their entire business as the pace of innovation in the area accelerates. According to a poll of pharma and life sciences professionals, 44 percent of them were adopting AI in their R&D efforts, which is expected to add $15.7 trillion to global output by 2030. Animal biotechnology, medical biotechnology, agricultural biotechnology, industrial biotechnology, and bioinformatics are some of the subcategories of biotechnology.
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AI in agricultural biotechnology
Plants that have been genetically altered through agricultural biotechnology can be used to improve crop yields or introduce new traits to already existing plants. Tissue culture and micropropagation are part of conventional plant breeding and molecular breeding, and genetic engineering. Autonomous agricultural robots that can harvest crops considerably faster than humans are already being developed and programmed by biotechnology corporations using Artificial Intelligence and Machine Learning techniques. Drones use computer vision and deep learning algorithms to analyze and evaluate the data they collect. Monitoring crop and soil health are made more accessible by this method. Machine Learning algorithms can be used to track and anticipate environmental changes, such as weather shifts that affect crop yields.
AI in medical biotechnology
Medical biotechnology uses living cells to produce medications and antibiotics for the benefit of human health. Genetically manipulating cells to boost the development of favorable traits is also part of this process. Drug discovery is heavily influenced by AI and Machine Learning. Machine Learning can aid in the discovery of tiny compounds that may have therapeutic advantages based on known target structures. Since the more diagnostic tests that are performed, the more accurate their results can be, machine learning is commonly employed in the diagnosis of diseases. Another area where AI is making a difference is in the radiation therapy planning process. EHRs with evidence-based medicines and clinical decision support systems are another area where Artificial Intelligence and Machine Learning are showing promise.
AI in animal biotechnology
Genetic engineering/modification of animals for pharmacological, industrial, or agricultural applications using molecular biology techniques is the focus of this field. Artificial Intelligence and machine learning models can provide significant information in the breeding of animals. Animals with desirable characteristics are paired together to produce offspring with the same traits, a procedure known as "selective breeding." On the molecular level, this method is used to select and breed animals with certain genetic features. Large genomic data sets are being analyzed using machine learning, and a wide range of genomic sequence elements are being annotated.
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