The mass spectrometer can detect different structures of the sugar molecules, called glycans, in our cells. The structures can indicate different forms of cancer in the cells. Credit: Lundberg Research Foundation/Magnus Gotander Researchers at the University of Gothenburg have created an AI model that enhances cancer detection capabilities through sugar analysis.
This AI model outperforms the existing semi-manual techniques in speed and accuracy in identifying abnormalities. Mass spectrometry can be used to measure glycans, which are sugar molecule structures in our cells. These structures can reveal the presence of various types of cancer within the cells.
However, the data from the mass spectrometer measurement must be carefully analyzed by humans to work out the structure from the glycan fragmentation. This process can take anywhere from hours to days for each sample and can only be carried out with high confidence by a small number of experts in the world, as it is essentially detective work learned over many years. Automating the detective work The process is thus a bottleneck in the use of glycan analyses, for example for cancer detection, when there are many samples to be analyzed.
Researchers at the University of Gothenburg have developed an AI model to automate this detective work. The AI model, named Candycrunch, solves the task in just a few seconds per test. The results are reported in a scientific article in the journal Nature Methods .
Daniel Bojar, Associate Se.