Singapore, March 7 -- Identifying therapeutic gene targets is essential for advancing personalised medicine and addressing the genetic basis of diseases. However, traditional experimental methods for discovering these targets are costly and time-consuming. While deep learning has shown promise in identifying biomarker genes, it has struggled to identify therapeutic genes.

To address this challenge, researchersfrom PusanNational University,South Korea have developed an innovative method, the Hypergraph Interactive Transformer (HIT), which accurately and quickly identifies therapeutic gene targets using hypergraphs and attention-based learning.

The HIT model utilises hypergraphs, which, unlike traditional graphs, can connect multiple node...