Applying machine learning to genome-wide association and electronic health record data may usher in a new era of precision in ...

Another investigation compared traditional and machine learning approaches in the analysis of time-to-event data, using weight status and breast cancer incidence as a case study.

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using ...

A machine learning model bests traditional methods for predicting cirrhosis mortality among hospitalized patients.

Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds. Advanced Intelligent Systems, 2024; DOI: 10.1002/aisy.202400253 ...

Imagine having a super-powered lens that uncovers hidden secrets of ultra-thin materials used in our gadgets. Research led by ...

All deep learning models are built on fundamental computational units that take in inputs, process them, and produce outputs—either forwarding them to the next layer or using them as the final ...

Physicists use machine learning throughout almost all parts of data collection and analysis. But what if machine learning could be used to optimize the experiment itself? “That’s the dream,” Watts ...

"Machine learning algorithm brings long-read sequencing to the clinic." ScienceDaily. ScienceDaily, 30 May 2025. <www.sciencedaily.com / releases / 2025 / 05 / 250529124849.htm>.

Planet Labs PBC (NYSE: PL), a leading provider of daily data and insights about Earth, today released Analysis-Ready PlanetScope (ARPS). ARPS harnesse ...

Training the LLM involved fine-tuning a Llama 2 (7B) model using Meta's historical investigation data, which helped the model learn to follow root cause analysis (RCA) instructions.

Recent progress in survival analysis has been driven by the integration of machine learning techniques with traditional statistical models, such as the Cox proportional hazards model. This ...