English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In many high-throughput experimental design settings, such as those common inbiochemical engineering, batched queries are more cost effective thanone-by-one sequential queries. Furthermore, it is often not possible todirectly choose items to query. Instead, the experimenter specifies a set ofconstraints that generates a library of possible items, which are then selectedstochastically. Motivated by these considerations, we investigate \emph{BatchedStochastic Bayesian Optimization} (BSBO), a novel Bayesian optimization schemefor choosing the constraints in order to guide exploration towards items withgreater utility. We focus on \emph{site-saturation mutagenesis}, a prototypicalsetting of BSBO in biochemical engineering, and propose a natural objectivefunction for this problem. Importantly, we show that our objective function canbe efficiently decomposed as a difference of submodular functions (DS), whichallows us to employ DS optimization tools to greedily identify sets ofconstraints that increase the likelihood of finding items with high utility.Our experimental results show that our algorithm outperforms common heuristicson both synthetic and two real protein datasets.

Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design