By making the source code of the projects available to other researchers and academics, IBM is hoping to maximise their potential impact.
Researchers from IBM’s Computational Systems Biology group in Zurich are using artificial intelligence (AI) and machine learning (ML) to help improve our understanding of cancers and their treatment.
The tech giant has also made three such projects available to the open source community to allow other researchers and academics to contribute to their development.
“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 said.
IBM will explain each of the projects in detail at the 18th European Conference on Computational Biology (ECCB) and the 27th Conference on Intelligent Systems for Molecular Biology (ISMB), which is scheduled to be held in Switzerland later this month.
Open source projects to fight cancer
The first project, dubbed PaccMann, is described as the “Prediction of anticancer compound sensitivity with Multi-modal attention-based neural networks.” The PaccMann algorithm is designed to automatically analyze chemical compounds and identify the one which is most likely to fight cancer strains.
IBM says identifying potential anti-cancer compounds earlier can help cut the costs associated with drug development.
The second project is called “Interaction Network infErence from vectoR representATions of words” or INtERAcT.
This tool aims to make the academic research easier by automatically extracting information from valuable scientific papers. Currently, 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.
The source code are now available on the projects’ websites.