How is machine learning affecting genetic testing?

Machine learning is being applied to genetic testing in many different ways.

The applications are nearly endless. Machine learning is helping scientists to analyze DNA, decode the human genome, assess disease phenotypes, understand gene expression, and even participate in a process called gene editing, where DNA is actually “spliced” into an organism’s genetic code.

The methods of computer science used in genetic machine learning also vary a good deal. Some projects use supervised learning, where all of the data is previously labeled. Others use unsupervised learning, which builds from unlabeled data sets, or a mix of the two principles called semi-supervised learning.

Many of the consumer-facing genetic testing technologies that we see on the market are using some form of machine learning or artificial intelligence to function. For example, products that help to show individuals more about their genetic makeup may have benefited from machine learning in research and development, or in the ongoing analysis of specimens.

In many ways, genetic testing is it the perfect field for machine learning applications, partly because of the enormous volumes of data that these programs need to contend with. For example, working on the human genome involves deciphering billions of bits of information, and prior to the advent of machine learning, many of these tasks were pretty daunting.

For example, Google has a program called DeepVariant that scientists say can now be used to fully map the human genome – that can be used on the full spectrum of a person’s genetic information.

Agencies like the National Institutes of Health are documenting the many ways that machine learning and artificial intelligence contribute to better understanding of genetics and genomics, the branch of molecular biology that covers genetic science. There’s even a “school” of machine learning called evolutionism that covers many of the classified machine learning tasks relevant to genetic work. In the end, machine learning is acting as a catalyst for quicker and more diverse development in genetic research and engineering.