Medical researchers develop disease-predicting algorithm and cancer-fighting cells

Researchers have announced two significant medical advancements aimed at disease prediction and cancer treatment.

At the Dana-Farber Cancer Institute and Mass General Brigham, scientists developed Aladynoulli, an algorithm capable of simultaneously predicting the probability of a patient developing 348 different diseases. Published in Nature, the tool uses routine electronic health record data and genetic risk information to generate multi-disease predictions. The system utilizes probabilistic modeling and machine learning to identify 20 biological signatures, allowing for dynamic risk updates as patients age.

Separately, researchers at the University of California San Francisco (UCSF) have developed a method to manufacture dendritic cells in a laboratory. These cells are essential for training T-lymphocytes to recognize and attack tumors. By using induced pluripotent stem cells (iPSC), the team can produce large quantities of these instructor cells, overcoming the difficulty of obtaining sufficient natural dendritic cells from cancer patients.