Wefirst establish minimax lower bounds for the estimation error, given avector of privacyguarantees, and show that a linear estimator is (near) optimal.

IntroductionThe current methodologies for enzyme annotation primarily rely on established databases and classifications such as KEGG Orthology (KO), Enzyme Commission (EC) numbers, and Gene Ontology (GO) annotations, each with its specific focus and methodology. For instance, the EC system categorizes enzymes based on the chemical reactions they catalyze, providing a hierarchical numerical classification. KO links gene products to their functional orthologs across different species, whereas GO offers a broader ontology for describing the roles of genes and proteins in any organism. Despite their widespread use, these systems have notable limitations. The EC classification, while widely used, sometimes groups vastly different enzymes under the same category or subdivides similar ones excessively, based on the substrates they interact with--leading to ambiguities in enzyme function characterization.

Enzymes, as catalysts of biological systems, are the workhorses of various biological functions [35, 52, 13] (Figure 1a).

Apple and Google agree to change app stores after'effective duopoly' claim Apple and Google have agreed to make changes to their app stores in the UK following an intervention from the UK markets regulator. According to the Competition and Markets Authority (CMA), the tech giants have committed to not giving preferential treatment to their own apps and will be transparent about how others are approved for sale, among other agreements. It comes seven months after the regulator said Apple and Google had an effective duopoly in the UK over their dominance in the sector. The CMA's head Sarah Cardell said the proposed commitments will boost the UK's app economy and were the first of many measures. The ability to secure immediate commitments from Apple and Google reflects the unique flexibility of the UK digital markets competition regime and offers a practical route to swiftly address the concerns we've identified, she said.

Theoretically, we prove that SFB can learn an asymptotically-optimal predictor without test-domain labels. Empirically, we demonstrate the effectiveness of SFB on real and synthetic data.