Israeli researchers develop AI tool to decode cell responses to drugs

Unlike current sequencing technologies, scNET cuts through the data noise to reveal hidden biological patterns by combining single-cell gene data with gene interaction networks, said the team from the Tel Aviv University, Xinhua news agency reported.

In tests focused on immune T cells, which are crucial for fighting cancer, scNET detected how cancer treatments boosted the cells' ability to kill tumours -- a subtle effect previously masked by data noise, it said.

“scNET integrates single-cell sequencing data with networks that describe possible gene interactions, much like a social network, providing a map of how different genes might influence and interact with each other,” said Ron Sheinin, a doctoral student at the varsity.

“scNET enables more accurate identification of existing cell populations in the sample. Thus, it is possible to investigate the common behaviour of genes under different conditions and to expose the complex mechanisms that characterise the healthy state or response to treatments,” Sheinin added.

In the study, the team focused on a population of T cells.

“scNET revealed the effects of treatments on these T cells and how they became more active in their cytotoxic activity against the tumour, something that was not possible to discover before due to the high level of noise in the original data,” said the researchers in the paper published in the journal Nature.

The tool could accelerate drug development and improve disease understanding.

The researchers highlighted how AI tools like scNET could help decode complex cell behaviours and design targeted therapies.

“This is an excellent example of how artificial intelligence tools can help decipher biological and medical data, allowing us to gain new and significant insights,” the researchers said.