Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

AI’s biggest constraint isn’t algorithms anymore. It’s data…specifically, high-quality, forward-looking data. It is the “Rare ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...

As computing power has increased and data science has expanded into nearly every area of our lives, we have entered the age of the algorithm. While our personal and professional data is being compiled ...

shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area We trained and validated two-phase ML ...

Through data, algorithms communicate with their environments and get to “know about” and “learn from” what is happening around them. Algorithms without living data are no more than sheer mathematical ...