A new deep-learning system called Atomic Rotationally Equivariant Scorer (ARES) significantly improves the prediction of RNA structures over previous artificial intelligence (AI) models.
Stanford researchers have developed a framework describing an end-to-end approach that infers experimental properties directly from nucleic acid sequence, using a principled statistical mechanical representation of the structure ensemble.
mRNA_hotfix is heuristic approach to adapt a stabilized mRNA to code for a protein mutation variant substitutes mutated codons with codons that maintain low predicted degradation.
Stanford inventors from Professor Rhiju Das's lab have developed a method to optimize nucleic acids, including aptamers and messenger RNAs to be more effective in clinical settings.
Stanford researchers have developed an algorithm and web server to accelerate the synthesis of DNA and RNA molecules. Many modern medicine applications require 'on-demand' templates for DNA genes.