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Stanford researchers have developed an innovative nucleic acid amplification method that enables low-cost, multiplexed detection while quantitatively maintaining the original ratios of target genes after amplification.

MANIfold is a novel method developed for systematic, high throughput measurements of nucleic acid secondary structure.

Stanford researchers have developed a method using trained learning models to optimize synthetic DNA libraries for high-throughput molecular biology experiments.

A new deep-learning system called Atomic Rotationally Equivariant Scorer (ARES) significantly improves the prediction of RNA structures over previous artificial intelligence (AI) models.

As part of a portfolio of COVID-19 inspired innovations, Stanford researchers led by Dr.

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 researchers have developed a secured, invite-only version of their popular Eterna platform for designing mRNA vaccines.

Stanford researchers have developed a stochastic algorithm MC sampler for designing mRNAs showing improved translation in vitro.

Stanford researchers in Professor Rhiju Das's lab have devised a method called RNAMake to optimize nucleic acids, such as aptamers and messenger RNAs, and enhance their effectiveness for clinical settings.