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Predicting DNA Changes with AI

By CIOReview | Thursday, February 7, 2019

Healthcare is the market in which AI has had an impact that has gone beyond convenience and has a positive impact on human life. AI offers more benefits in comparison with traditional analysis and clinical decision-making. Learning algorithms can be more precise and accurate when interacting with training data, allowing people to gain unprecedented insights into diagnostics, treatment processes, treatment variability, and patient outcomes. Advances in AI can help to advance genomics by predicting the effects of changes in DNA structures. This can be used to support medical diagnostics, better vaccinations, and research into crop breeding.

Genomics is one area in which machine learning is significantly evolving. Gene analysis is crucial not only for its effects on human health but also on agriculture and animal husbandry. 

The paper, published in Nature Genetics, shows that AI can now understand and predict changes in the patterns of DNA structures in depth. These findings suggest that due to its speed and accuracy, the AI analysis of DNA structures exceeds human capabilities. Before there was an AI- accelerated analysis, scientists used laboratory research to analyze changing DNA structures. It is time consuming and expensive, with DNA sequences often subject to multiple changes at a time. On the other hand, using AI provides the ability to analyze DNA much faster.

Predicting the effects of changes in DNA sequences is essential in many genomics fields and is an integral part of new medical diagnoses, vaccine development, and plant breeding innovations. AI can help by finding patterns in large quantities of data that would have been difficult to see or understand for humans.

In genomics, researchers need to understand not only the effect of a single change in DNA but also several changes; this work is currently being carried out in laboratory experiments that are costly and time-consuming. With AI, researchers can save time and money by building an AI model that can do more with less and faster. The future of AI and gene technology is expected to include pharmacogenomics and newborn genetic screening tools.

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