Researchers from IBM’s Computational Systems Biology group in Zurich are working on AI and machine learning (ML) approaches to “help to accelerate our understanding of the leading drivers and molecular mechanisms of these complex diseases,” as well as methods to improve our knowledge of tumor composition.
“Our goal is to deepen our understanding of cancer to equip industries and academia with the knowledge that could potentially one day help fuel new treatments and therapies,” IBM says.
The first project, dubbed PaccMann — not to be confused with the popular Pac-Man computer game — is described as the “Prediction of anticancer compound sensitivity with Multi-modal attention-based neural networks.”
The ML algorithm exploits data on gene expression as well as the molecular structures of chemical compounds. IBM says that by identifying potential anti-cancer compounds earlier, this can cut the costs associated with drug development.
The second project is called “Interaction Network infErence from vectoR representATions of words,” otherwise known as INtERAcT. This tool is a particularly interesting one given its automatic extraction of data from valuable scientific papers related to our understanding of cancer.
With roughly 17,000 papers published every year in the field of cancer research, it can be difficult — if not impossible — for researchers to keep up with every small step we make in our understanding.
INtERAcT aims to make the academic side of research less of a burden by automatically extracting information from these papers. At the moment, the tool is being tested on extracting data related to protein-protein interactions — an area of study which has been marked as a potential cause of the disruption of biological processes in diseases including cancer.
The third and final project is “pathway-induced multiple kernel learning,” or PIMKL. This algorithm utilizes datasets describing what we currently know when it comes to molecular interactions in order to predict the progression of cancer and potential relapses in patients.
PIMKL uses what is known as multiple kernel learning to identify molecular pathways crucial for categorizing patients, giving healthcare professionals an opportunity to individualize and tailor treatment plans.
But now the big question: will they maintain it?