Biotechnology companies take advantage of AI and ML techniques to create and program autonomous robots that manage critical agricultural tasks such as harvesting crops faster than humans.
FREMONT, CA: Artificial Intelligence (AI) has played a significant role in transforming the biotechnology space. Firms integrating AI in their business are recognizing the benefits it can provide in the form of cost-effectiveness, vital predictions, expanding accessibility, and efficient decision-making.
Today, biotechnology scientists have access to vast amount of data and as such, it has become vital to depend on AL and machine learning (ML) to gather and extract data from various data sources, perform data analytics tasks and progress effectively.
Here are five branches of biotechnology that AI is impacting significantly:
In medical biotechnology, living cells are used to advance human health by creating drugs and antibiotics. It also studies DNA and genetically manipulates the cells to increase the production of vital and beneficial qualities.
AI and ML are widely used in drug discovery. ML assists in identifying small molecules that could give therapeutic advantages dependent on known target structures. It also uses the true result in diagnosing diseases to enhance diagnostic tests.
Leveraging AI can decrease the radiation therapy planning process, which saves time and enhance patient care. AI and ML also have potential uses in improving EHRs with evidence-based medicines and clinical decision support systems. These technologies are also extensively used in gene editing, radiology, medication management, and personalized medicine.
Agricultural biotechnology develops genetically modified plants for higher crop yield and brings new traits to existing plants. This involves standard plant breeding, tissue culture, micropropagation, molecular breeding, and genetic engineering of plants.
Biotechnology companies take advantage of AI and ML techniques to create and program autonomous robots that manage critical agricultural tasks such as harvesting crops faster than humans. Computer Vision and Deep Learning Algorithms are integrated to operate and evaluate data gathered by drones to track crop and soil health. ML algorithms can monitor and forecast different environmental changes that influence the crop yield.
Internet of Things (IoT), ML, and AI evaluates machines, forecast outages, enhance equipment to offer efficient production, and improved product quality. Robotics and ML develop the strains and test how far the desired molecule was reached.
AI and ML are implemented in DNA sequencing from the massive data pool, protein grouping with its catalytic role and biological function, analysis of gene expressions, computer-assisted drug design, and more.
Animal biotechnology uses biology techniques to genetically engineer or modify animals to enhance their sustainability, pharmaceutical, industrial or agricultural goals.
AI and ML models offer crucial insights into the breeding of animals. Selective breeding is a common practice where genetic traits among animals are selected and bred with each other so that their offspring will have the same traits. ML helps in understanding genomics and making decisions to help scientists predict the expression of those genes.
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