A new study from researchers at MUSC Hollings Cancer Center found that machine learning may help identify cancer patients at high risk of experiencing financial toxicity — the financial hardship and stress that often accompany cancer treatment. Using data from 793 patients, researchers tested six machine learning models and developed a web-based tool that can estimate a patient's likelihood of financial strain based on factors such as age, income, health status, treatment status and out-of-pocket costs. The best-performing model identified high-risk patients with 84% sensitivity and 75% specificity, offering a potential way for health care teams to intervene earlier with financial counseling and support services. The goal is to move financial toxicity screening from a reactive process to a proactive one, helping patients access resources before financial strain affects treatment decisions, quality of life or health outcomes.

Researchers at IRB Barcelona develop a computational framework that creates molecules with selective activity in specific cell types, without the need to start from a predefined molecular target.

Published in Communications Chemistry, the new strategy combines predictive and generative AI to design new chemical entities with specific biological effects.

Experimental validation confirmed that several of the AI-generated molecules displayed the activity they were designed for, achieving a success rate superior to that of traditional screening methods.

New viewpoint outlines how foundation models could transform the operation of robot swarms

Using bibliometric analysis of more than 1,000 articles published between 2014 and 2024, a new study charts the first decade of the Journal of Big Data and its emergence as a leading global data science publication. The journal has surpassed 1,000 papers and nearly 65,000 citations. Researchers identified three core themes – big data infrastructure, machine and deep learning, and applications such as cybersecurity, healthcare analytics, and sentiment analysis – while highlighting the journal’s growing international reach and influence on data science and AI research.