Categories
Technology

IBM Gives Cancer-Killing Drug AI Project To the Open Source Community

IBM has released three artificial intelligence (AI) projects tailored to take on the challenge of curing cancer to the open-source community. ZDNet reports: 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.” IBM is working on the PaccMann algorithm to automatically analyze chemical compounds and predict which are the most likely to fight cancer strains, which could potentially streamline this process. 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. 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.


Share on Google+

View source

Codice amico Very Mobile Diagonal Media Digital Marketing