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The complexity of seeking to cure cancer has troubled researchers for decades. Although they have made significant progress, they are still fighting for it, as cancer remains one of the major causes of death worldwide.
However, scientists may soon have a key new ally around them - artificial intelligence machines - that can solve these complexities in different ways.
Consider an example from the game world: Last year, Google's AI platform, Deploy Depth Learning technology, defeated the world's top chess master South Korea's famous Go players Li Shishi, Go is an extremely complex game, each step is more likely to go There are more stars in the universe.
These same techniques of machine learning and artificial intelligence can also come to the massive scientific puzzles that solve cancer.
One thing is for sure - if we do not have more data to use, we will not be able to use these new technologies to conquer cancer. For example, many data sources, including medical records, genetic tests, and mammograms, can not be exploited by the best scientific ideas and best learning algorithms if they are blocked.
The good news is that the role of big data in cancer research is now at a major stage of development and some large-scale government-led gene sequencing programs are moving forward. These include the US Department of Veterans Affairs Million Veterans Program; the UK's 100,000 Genome Project; and NIH's cancer genome map, which holds data from over 11,000 patients and is made available to researchers anywhere through cloud analysis . According to a recent study, up to 2 billion human genomes can be sequenced by 2025.
There are other trends that push the demand for new data, including genetic testing. In 2007, a person's genome was sequenced at $ 10 million. You only need to spend less than 1,000 U.S. dollars today. In other words, sort a person 10 years ago and we can now do 10,000. The impact is great: finding that you have a higher risk of a mutation or getting some types of cancer can sometimes be a life-saving message. And with more and more investment, research is also facing a huge potential scale.
A core challenge for researchers (and society) is the lack of quantitative and ethnic diversity in the current data set. In addition, researchers often face restrictive legal terminology and unwillingness to share partnerships. Even though organizations share genomic data sets, protocols typically target individual data sets between individual agencies. Although larger clearinghouses and databases have done a great job at the moment, we need more standardized terminology and platform work to speed up access.
The potential benefits of these new technologies go beyond identifying risks and screening. Advances in machine learning can help speed up the development and treatment options for cancer drugs, allowing physicians to match patients to clinical trials and increase their ability to deliver customized treatment plans for cancer patients (Herceptin, one of the earliest examples, is still One of the best examples).
We believe there are three things that need to happen to make the data more useful for cancer research and AI programs. First, patients should be able to easily contribute data. This includes medical records, radiology images, and genetic tests. Laboratory companies and medical centers should adopt a common consent form that makes data sharing easy and legal. Second, researchers in the field of artificial intelligence, who work on the link between data science and cancer, need more money. Just as the Chan Zuckerberg Foundation funds the development of new medical tools, new artificial intelligence technologies need funding for medical applications. Third, new data sets should be generated, with a focus on people of all races. We need to ensure that everyone has access to the advances in cancer research.
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