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<p>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.</p>
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<li>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.<br /> &nbsp;</li>
<li>Machine learning analyses could benefit disease research and genomic tools like CRISPR.<br /> &nbsp;</li>
<li>NHGRI is identifying and shaping its unique role in the convergence of genomic and machine learning research.</li>
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<h2>What are some ways in which AI/ML are being used in genomics?</h2>
<p>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.</p>
<p>Some examples include:</p>
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<li>Examining people&rsquo;s faces with facial analysis AI programs to accurately <a href="https://www.nature.com/articles/s41591-018-0279-0">identify genetic disorders</a>.</li>
<li>Using machine learning techniques to <a href="https://www.nature.com/articles/s41586-019-1272-6">identify the primary kind of cancer</a> from a liquid biopsy.</li>
<li>Predicting <a href="https://www.nature.com/articles/s41592-018-0108-x">how a certain kind of cancer will progress in a patient</a>.</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237276/">Identifying disease-causing genomic variants</a> compared to benign variants using machine learning.</li>
<li>Using deep learning to <a href="https://www.nature.com/articles/nbt.4061">improve the function of gene editing tools such as CRISPR</a>.<br /> &nbsp;</li>
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<p>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.</p>
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<h2 class="heading">What is NHGRI&rsquo;s role in bringing AI, machine learning and genomics together?</h2>
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<p>NHGRI&rsquo;s Genomic Data Science Working Group collaborates closely with NIH and other academic institutes to define critical areas in genomics for AI and machine learning. The group is also helping to define NHGRI&rsquo;s unique role in enabling machine learning research to assist in both genomic sciences and genomic medicine. In April 2021, NHGRI hosted a <a href="https://www.youtube.com/watch?v=lHXg_OeQIRk&amp;list=PL1ay9ko4A8slpiTxI_mmmRGxG5_JPhhEW" target="_blank">virtual workshop on machine learning in genomics</a> which put forth a vast array of promising advances at the intersection of artificial intelligence and genomics research.</p>
<p>NHGRI is also a key part of NIH&rsquo;s new Common Fund program, <a href="https://commonfund.nih.gov/bridge2ai">Bridge to Artificial Intelligence (Bridge2AI)</a>. The goal of the Bridge2AI program is to act as a launchpad for the widespread adoption of AI in tackling complex biomedical and precision medicine challenges.</p>
<p>Apart from being part of Bridge2AI, NHGRI also independently funds research at the intersection of AI/ML and genomics, and is particularly focused on ensuring that the genomic data used in AI and deep learning programs appropriately reflect fairly and ethically on the diversity of the human species. The NHGRI also supports research on the <a href="https://www.genome.gov/Funded-Programs-Projects/ELSI-Research-Program-ethical-legal-social-implications">ethical, legal and social implications</a> of the use of AI/ML in genomics.</p>
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Lista tag dell'oggetto (oggettag)
cod_tag	value
ai	5
gen	5
genoma	5
genomics	5
machine learning	5
ml	5
wpg	5

Lista delle righe dell'oggetto (oggettir)
tip_pro	cod_pro	descrizione	val_pro	pes_pro

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