It’s a rare angle for those who’ve been raised on a heady diet of movies where the robot goes mad and tries to kill all humans: an artificial intelligence using its abilities to help humankind! But that’s the idea being explored by researchers like Raul Rabadan, a theoretical physicist working in biology at Columbia University. Using a new form of machine learning, they are seeking to unlock the mysteries of flu strains.
Basically, they are hoping to find out why flu strains like the H1N1, which ordinarily infect pigs and cows, are managing to make the jump to human hosts. Key to understanding this is finding the specific mutations that transform it into a human pathogen. Traditionally, answering this question would require painstaking comparisons of the DNA and protein sequences of different viruses.
But thanks to rapidly growing databases of virus sequences and advances made in computing, scientists are now using sophisticated machine learning techniques — a branch of artificial intelligence in which computers develop algorithms based on the data they have been given — to identify key properties in viruses like bird flu and swine flu and seeing how they go about transmitting from animals to humans.
This is especially important since every few decades, a pandemic flu virus emerges that not only infects humans but also passes rapidly from person to person. The H7N9 avian flu that infected more than 130 people in China is just the latest example. While it has not been as infectious as others, the fact that humans lack the antibodies to combat it led to a high lethality rate, with 44 of the infected dying. Whats more, it is expected to emerge again this fall or winter.
Knowing the key properties to this and other viruses will help researchers identify the most dangerous new flu strains and could lead to more effective vaccines. Most importantly, scientists can now look at hundreds or thousands of flu strains simultaneously, which could reveal common mechanisms across different viruses or a broad diversity of transformations that enable human transmission.
Researchers are also using these approaches to investigate other viral mysteries, including what makes some viruses more harmful than others and factors influencing a virus’s ability to trigger an immune response. The latter could ultimately aid the development of flu vaccines. Machine learning techniques might even accelerate future efforts to identify the animal source of mystery viruses.
This technique was first employed in 2011 by Nir Ben-Tal – a computational biologist at Tel Aviv University in Israel – and Richard Webby – a virologist at St. Jude Children’s Research Hospital in Memphis, Tennessee. Together, Ben-Tal and Webby used machine learning to compare protein sequences of the 2009 H1N1 pandemic swine flu with hundreds of other swine viruses.
Machine learning algorithms have been used to study DNA and protein sequences for more than 20 years, but only in the past few years have scientists applied them to viruses. Inspired by the growing amount of viral sequence data available for analysis, the machine learning approach is likely to expand as even more genomic information becomes available.
As Webby has said, “Databases will get much richer, and computational approaches will get much more powerful.” That in turn will help scientists better monitor emerging flu strains and predict their impact, ideally forecasting when a virus is likely to jump to people and how dangerous it is likely to become.
Perhaps Asimov had the right of it. Perhaps humanity will actually derive many benefits from turning our world increasingly over to machines. Either that, or Cameron will be right, and we’ll invent a supercomputer that’ll kill us all!