Using data science to beat cancer

Using data science to beat cancer

Using data science to beat cancer

The complexity of seeking a cure for cancer has vexed researchers for decades. While they’ve made remarkable progress, they are still waging a battle uphill as cancer remains one of the leading causes of death worldwide.

Yet scientists may soon have a critical new ally at their sides —  intelligent machines — that can attack that complexity in a different way.

Consider an example from the world of gaming: Last year, Google’s artificial intelligence platform, AlphaGo, deployed techniques in deep learning to beat South Korea Grand Master Lee Sedol in the immensely complex game of Go, which has more moves than there are stars in the universe.

Those same techniques of machine learning and AI can be brought to bear in the massive scientific puzzle of cancer.

One thing is certain — we won’t have a shot at conquering cancer with these new methods if we don’t have more data to work with. Many data sets, including medical records, genetic tests and mammograms, for example, are locked up and out of reach of our best scientific minds and our best learning algorithms.

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The good news is that big data’s role in cancer research is now at center stage, and a number of large-scale, government-led sequencing initiatives are moving forward. Those include the U.S. Department of Veteran Affairs’ Million Veteran Program; the 100,000 Genomes Project in the U.K.; and the NIH’s The Cancer Genome Atlas, which holds data from more than 11,000 patients and is open to researchers everywhere to analyze via the cloud. According to a recent study, as many as 2 billion human genomes could be sequenced by 2025.

There are other trends driving demand for fresh data, including genetic testing. In 2007, sequencing one person’s genome cost $10 million. Today you can get this done for less than $1,000. In other words, for every person sequenced 10 years ago, we can now do 10,000.

 



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