Earlier this year, we looked at a story about a Washington State University chemistry professor who claimed to have adapted Google’s PageRank algorithm to help determine molecular shapes and chemical reactions.
We also interviewed her here.
Then, we showed you a study that looked at improving outcome prediction for cancer patients by network-based ranking of marker genes, using Google’s PageRank concept.
Now, the part of Google’s algorithm that’s used to predict the pages users visit is being utilized by a mathematician named Paul Newton from USC to track the spread of cancer cells throughout the body. LiveScience reports:
Google ranks Web pages by the likelihood that an individual would end up visiting each one randomly. These predictions are based on the trends of millions of users across the Web. Google uses something called the “steady state distribution” to calculate the probability of someone visiting a page.
“You have millions of people wandering the Web, [and] Google would like to know what proportion are visiting any given Web page at a given time,” Newton explained.
“It occurred to me that steady state distribution is equivalent to the metastatic tumor distribution that shows up in the autopsy datasets.”
Google’s Matt Cutts shared the news on Twitter:
Using PageRank-like computation on the way cancer spreads: http://t.co/eMge29V9
If only there were a Penguin or Panda-type update that could simply eliminate cancerous cells.