Researchers use deep learning method to delve into predicting RNA structures in world first

In a world-first, a team of Griffith University researchers has used an artificial intelligence method to better predict RNA secondary structures, with the hope it can be developed into a tool to better understand how RNAs are implicated in various diseases such as cancer. Professor Yaoqi Zhou, Professor Kuldip Paliwal, Ph.D. student Jaswinder Singh and Dr. Jack Hanson from Griffith's Institute for Glycomics and Signal Processing Laboratory led the research, which has been published in Nature Communications. In all forms of life, ribonucleic acid (RNA) is essential for the coding, decoding, regulation and expression of genes. RNA and DNA are among the four major macromolecules in lifeforms. The team employed the use of deep learning--a subset of artificial intelligence used to create complex, numerical functions to approximate specific tasks automatically without explicit human instructions--to build a more accurate model of the relationship between RNA sequence and structure.