Non-coding RNA Structure Analysis Based on Deep Neural Networks

点击翻译按钮获取中文摘要

The main objective of this collaboration is to develop new machine learning algorithms to analyze non-coding ribonucleic acid (ncRNA) structures based on recent developments in deep neural networks. ncRNAs play important roles in cell processes such as to effect high-order chromosomal dynamics, telomere biology, subcellular structural organization, and gene expression. Analyzing ncRNA structure is a fundamental step towards understanding their biological functions. Through this collaboration with the University of Kansas, this project addresses a very important interdisciplinary problem that involves ncRNA, computational biology, and machine learning. It will provide a new bioinformatics infrastructure for sequencing data analysis which will have significant research impacts in biomedicine, biology, RNA therapeutics, and drug discovery, while promoting Canada's global leadership in bioengineering and artificial intelligence.