AI Unlocks Potential for Genetic Analysis
The GEEK approach represents a novel way to study gene expression by AI.
A research team led by Professor Kevin Yip from the Department of Computer Science and Engineering has developed a new Gene Expression Embedding frameworK (GEEK), which uses artificial intelligence technologies in machine learning and natural language processing to study the regulation of gene expression. The new framework may shed light on the causes of cancers and their treatment. The study was published in the renowned international science journal Nature Machine Intelligence.
‘Gene expression’ refers to the different gene activities in the DNA sequences that take place in the trillions of cells contained in each human body.
Professor Yip and his team use machine learning and natural language processing methods that treat genes as ‘words’ to capture their relationships in ‘sentences’. Unlike previous studies on gene expression that focused on only one or a few regulatory mechanisms at a time, this new framework can study the joint effects of many mechanisms simultaneously.
Professor Yip used a metaphor to explain the intricate relationships among gene regulatory mechanisms. ‘If you fail to turn on an electronic appliance using a remote controller, it seems like there is a problem with the controller, but the problem may also lie with the receiver or compatibility issues between the two. If we have a tool that can analyse the different components at the same time, it would be much easier to identify the root of the problem.’
Leveraging AI in Medical Research
Professor Yip hopes that artificial intelligence can be used in the future to predict patients’ responses to immunotherapies, which would improve treatment precision and reduce the burden on patients.
Cancer is caused by mutations that lead to abnormal cell proliferation. The GEEK approach represents a novel way to study gene expression in different types of cells, including cancer cells. ‘We will work closely with medical experts to try explaining some causes of liver cancer using GEEK,’ Professor Yip said. ‘In the long run, we hope to extend our research to other cancer types and contribute to the development of new prevention and treatment methods.’
The research project took place over a one-and-a-half-year period and was supported by the University Grants Committee of Hong Kong. Professor Yip has more than ten years of experience in gene regulation research, and he is one of the first to use machine learning and natural language processing to study gene regulation.