The model, called TREE, is based on the Transformer architecture, which uses machine learning to analyze data on biological molecules. It not only identifies the most important types of gene data, but also finds key pathways that regulate genes associated with cancer development. The study was published in the journal Nature Biomedical Engineering.
The main difference between TREE and traditional models is that it takes into account the structure of biological networks, which can significantly increase the accuracy of predictions. The model uses multimodal data, including information about genes and other molecules, and takes into account the structure of this data, helping to better understand complex interactions in biological systems.
TREE also uses a convergent attention mechanism to better describe the global structure of data and identify hidden patterns. In this way, the model significantly increases the accuracy of predictions for identifying genes associated with cancer progression, which opens new hopes for the development of personalized treatments.
Source: Ferra
I am a professional journalist and content creator with extensive experience writing for news websites. I currently work as an author at Gadget Onus, where I specialize in covering hot news topics. My written pieces have been published on some of the biggest media outlets around the world, including The Guardian and BBC News.
