Evolutionary algorithms (EAs) represent a class of heuristic optimisation methods inspired by natural selection and Mendelian genetics. They iteratively evolve a population of candidate solutions ...

A computer algorithm can efficiently find genetic mutations that work together to drive cancer as well as other important genetic clues that researchers might someday use to develop new treatments for ...

Proteogenomics explores how genetic information translates into protein expression and function, and the role of changes across DNA, RNA, and proteins in influencing disease development and ...

Dr. James McCaffrey of Microsoft Research explains stochastic gradient descent (SGD) neural network training, specifically implementing a bio-inspired optimization technique called differential ...

Artificial Intelligence (AI) has become a potent tool in identifying genetic mutations. By leveraging AI capabilities, scientists can rapidly and accurately analyze vast amounts of data. This ...