Taking Cues From Speech Recognition, New Machine Learning Algorithm Finds Patterns in RNA Structures

By Greg Watry

Software inspired by speech recognition technology could help scientists understand the secret language inside cells. A machine learning algorithm called patteRNA, designed by UC Davis researchers, rapidly mines ribonucleic acid, commonly called RNA, for specific structures, providing a new method to establish links between structure, function and disease.

The study, co-authored by integrative genetics and genomics Ph.D. student Mirko Ledda and Assistant Professor Sharon Aviran, UC Davis Genome Center, appears in Genome Biology.

Deciphering the biological role of RNA structures

RNA is essential to all biological processes, from gene expression and regulation to protein synthesis. While DNA stores an organism’s genetic information, RNA puts that genetic information to use.

NSF Grant Funds Math For National Security

Applying mathematics to detect chemical weapons, hidden explosives or other threats is the goal of an ongoing project at the UC Davis Department of Mathematics, supported by grants from the National Science Foundation.

Resolving blurred image with math

Blind deconvolution is a mathematical method to clarify a blurred image without knowledge of the original image, or how it was blurred. Top, original image; bottom, blurred image after blind deconvolution (Original image by Steve Byland).

Threat detection involves math at a range of levels, said Professor Thomas Strohmer, who leads the project. It can include quickly processing large amounts of data, coordinating multiple sensors, or extracting clarity from background noise.