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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

Tens of Millions Saved

The Associated Press writes that vaccines against COVID-19 saved an estimated 20 million lives in their first year.

Supersized Bacterium

NPR reports that researchers have found and characterized a bacterium that is visible to the naked eye.

Also Subvariants

Moderna says its bivalent SARS-CoV-2 vaccine leads to a strong immune response against Omicron subvariants, the Wall Street Journal reports.

Science Papers Present Gene-Edited Mouse Models of Liver Cancer, Hürthle Cell Carcinoma Analysis

In Science this week: a collection of mouse models of primary liver cancer, and more.