Big Data is everywhere, and we are constantly told that it holds the answers to almost any problem we want to solve. Companies collect information on how we shop, doctors and insurance companies gather our medical test results, and governments compile logs of our phone calls and emails. In each instance, the hope is that critical insights are hidden deep within massive amounts of information, just waiting to be discovered.
Two researchers at Cold Spring Harbor Laboratory challenge the most recent advances in this Big Data analysis, using a classic mathematical concept to tackle the outstanding problems in this field. Mutual information is able to uncover patterns in large lists of numbers, revealing entirely new, unexpected patterns.
But simply having lots of data is not the same as understanding it. Increasingly, new mathematical tools are needed to extract meaning from enormous data sets. In work published online today, two researchers at Cold Spring Harbor Laboratory (CSHL) now challenge the most recent advances in this field, using a classic mathematical concept to tackle the outstanding problems in Big Data analysis.
What does it mean to analyze Big Data? A major goal is to find patterns between seemingly unrelated quantities, such as income and cancer rates. Many of the most common statistical tools are only able to detect patterns if the researcher has some expectation about the relationship between the quantities. Part of the lure of Big Data is that it may reveal entirely new, unexpected patterns. Therefore, scientists and researchers have worked to develop statistical methods that will uncover these novel relationships.
In 2011, a distinguished group of researchers from Harvard University published a highly influential paper in the journal Science that advanced just such a tool. But in a paper published today in Proceedings of the National Academy of Sciences, CSHL Quantitative Biology Fellow Justin Kinney and CSHL Assistant Professor Gurinder "Mickey" Atwal demonstrate that this new tool is critically flawed. "Their statistical tool does not have the mathematical properties that were claimed," says Kinney.
Kinney and Atwal show that the correct tool was hiding in plain sight all along. The solution, they say, is a well known mathematical measure called "mutual information," first described in 1948. It was initially used to quantify the amount of information that could be transmitted electronically through a telephone cable; the concept now underlies the design of the world's telecommunications infrastructure. "What we've found in our work is that this same concept can also be used to find patterns in data," Kinney explains.
Applied to Big Data, mutual information is able to reveal patterns in large lists of numbers. For instance, it can be used to analyze patterns in data sets on the numerous bacterial species that help us digest food. "This particular tool is perfect for finding patterns in studies of the human microbiome, among many other things," Kinney says.
Importantly, mutual information provides a way of identifying all types of patterns within the data without reliance upon any prior assumptions. "Our work shows that mutual information very naturally solves this critical problem in statistics," Kinney says. "This beautiful mathematical concept has the potential to greatly benefit modern data analysis, in biology and in biology and many other important fields.
The research described here was supported by the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory.
"Equitability, mutual information, and the maximal information coefficient" appears online in PNAS on February 17, 2014. The authors are: Justin Block Kinney and Gurinder Singh Atwal. The paper can be obtained online at: http://www.pnas.org/content/early/2014/02/14/1309933111.abstractAbout Cold Spring Harbor Laboratory
Jaclyn Jansen | EurekAlert!
The Flexible Grid Involves its Users
27.09.2016 | Fraunhofer-Institut für Angewandte Informationstechnik FIT
Optical fiber transmits one terabit per second – Novel modulation approach
16.09.2016 | Technische Universität München
Friction stir welding is a still-young and thus often unfamiliar pressure welding process for joining flat components and semi-finished components made of light metals.
Scientists at the University of Stuttgart have now developed two new process variants that will considerably expand the areas of application for friction stir welding.
Technologie-Lizenz-Büro (TLB) GmbH supports the University of Stuttgart in patenting and marketing its innovations.
Friction stir welding is a still-young and thus often unfamiliar pressure welding process for joining flat components and semi-finished components made of...
Optical quantum computers can revolutionize computer technology. A team of researchers led by scientists from Münster University and KIT now succeeded in putting a quantum optical experimental set-up onto a chip. In doing so, they have met one of the requirements for making it possible to use photonic circuits for optical quantum computers.
Optical quantum computers are what people are pinning their hopes on for tomorrow’s computer technology – whether for tap-proof data encryption, ultrafast...
The Fraunhofer Institute for Organic Electronics, Electron Beam and Plasma Technology FEP has been developing various applications for OLED microdisplays based on organic semiconductors. By integrating the capabilities of an image sensor directly into the microdisplay, eye movements can be recorded by the smart glasses and utilized for guidance and control functions, as one example. The new design will be debuted at Augmented World Expo Europe (AWE) in Berlin at Booth B25, October 18th – 19th.
“Augmented-reality” and “wearables” have become terms we encounter almost daily. Both can make daily life a little simpler and provide valuable assistance for...
With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. They report on their findings in the scientific journal Physical Review Letters.
Elpasolite is a glassy, transparent, shiny and soft mineral with a cubic crystal structure. First discovered in El Paso County (Colorado, USA), it can also be...
For the first time, Fraunhofer IKTS shows additively manufactured hardmetal tools at WorldPM 2016 in Hamburg. Mechanical, chemical as well as a high heat resistance and extreme hardness are required from tools that are used in mechanical and automotive engineering or in plastics and building materials industry. Researchers at the Fraunhofer Institute for Ceramic Technologies and Systems IKTS in Dresden managed the production of complex hardmetal tools via 3D printing in a quality that are in no way inferior to conventionally produced high-performance tools.
Fraunhofer IKTS counts decades of proven expertise in the development of hardmetals. To date, reliable cutting, drilling, pressing and stamping tools made of...
30.09.2016 | Event News
29.09.2016 | Event News
28.09.2016 | Event News
30.09.2016 | Earth Sciences
30.09.2016 | Event News
30.09.2016 | Physics and Astronomy