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It's Likelihood-Free

Researchers led by Imperial College London's Oliver Ratmann developed a new model-based way to use Bayesian inference to study biological network data. In PLoS Computational Biology, they report that their approach uses the Approximate Bayesian Computation, or Likelihood-Free Inference and a MCMC algorithm to ascertain the distribution of the model's parameters. The researchers then used their approach to model gene duplication in Helicobacter pylori and Plasmodium falciparum and found that gene duplication plays more of a role in eukaryotic network evolution than in that of prokaryotes.

The Scan

Billions for Antivirals

The US is putting $3.2 billion toward a program to develop antivirals to treat COVID-19 in its early stages, the Wall Street Journal reports.

NFT of the Web

Tim Berners-Lee, who developed the World Wide Web, is auctioning its original source code as a non-fungible token, Reuters reports.

23andMe on the Nasdaq

23andMe's shares rose more than 20 percent following its merger with a special purpose acquisition company, as GenomeWeb has reported.

Science Papers Present GWAS of Brain Structure, System for Controlled Gene Transfer

In Science this week: genome-wide association study ties variants to white matter stricture in the brain, and more.