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Artificial Intelligence, Machine Learning and Genomics
With increasing complexity in genomic data, researchers are turning to artificial intelligence and machine learning as ways to identify meaningful patterns for healthcare and research purposes.
- The genomics field continues to expand the use of computational methods such as artificial intelligence and machine learning to improve our understanding of hidden patterns in large and complex genomics data sets from basic and clinical research projects.
- Machine learning analyses could benefit disease research and genomic tools like CRISPR.
- NHGRI is identifying and shaping its unique role in the convergence of genomic and machine learning research.
What are some ways in which AI/ML are being used in genomics?
Although the use of AI/ML tools in genomics is still at an early stage, researchers have already benefited from developing programs that assist in specific ways.
Some examples include:
- Examining people’s faces with facial analysis AI programs to accurately identify genetic disorders.
- Using machine learning techniques to identify the primary kind of cancer from a liquid biopsy.
- Predicting how a certain kind of cancer will progress in a patient.
- Identifying disease-causing genomic variants compared to benign variants using machine learning.
- Using deep learning to improve the function of gene editing tools such as CRISPR.
These are just a few ways by which AI/ML methods are helping predict and identify hidden patterns in genomic data. Scientists are also using AI/ML to predict future variations in the genomes of the influenza and SARS-CoV-2 viruses to assist public health efforts.