Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine ...

The approach combines a new genetic algorithm using empirical interatomic potentials to explore the configurational phase space of boundaries, and thereafter refining structures and free energies ...

Typically, standard genetic algorithms use a form of bit representation, while evolutionary algorithms use real values, but this vocabulary distinction is arbitrary.

moPepGen leverages a graph-based approach to improve the detection of hidden protein variants in a computationally efficient manner.

By analyzing thousands of genetic risk variants, a new algorithm may be able to predict your risk of developing chronic kidney disease decades before onset.

An AI algorithm based on the genomic database was then used to analyse the DNA of 454,000 human participants in the UK Biobank project, with the results showing “greatly improved genetic risk ...

The MMRDetect clinical algorithm makes it possible to identify tumours that have 'mismatch repair deficiencies' and then improve the personalisation of cancer therapies to exploit those weaknesses.

Ongoing research in T2D genetics and associated risk-prediction models is necessary to refine genetic risk pathways, algorithms for risk prediction and use of this information in clinical care.