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

Despite experimental and curation efforts, the extent of enzyme promiscuityon substrates continues to be largely unexplored and under documented.Recommender systems (RS), which are currently unexplored for theenzyme-substrate interaction prediction problem, can be utilized to provideenzyme recommendations for substrates, and vice versa. The performance ofCollaborative-Filtering (CF) recommender systems however hinges on the qualityof embedding vectors of users and items (enzymes and substrates in our case).Importantly, enhancing CF embeddings with heterogeneous auxiliary data,specially relational data (e.g., hierarchical, pairwise, or groupings), remainsa challenge. We propose an innovative general RS framework, termed Boost-RS,that enhances RS performance by "boosting" embedding vectors through auxiliarydata. Specifically, Boost-RS is trained and dynamically tuned on multiplerelevant auxiliary learning tasks Boost-RS utilizes contrastive learning tasksto exploit relational data. To show the efficacy of Boost-RS for theenzyme-substrate prediction interaction problem, we apply the Boost-RSframework to several baseline CF models. We show that each of our auxiliarytasks boosts learning of the embedding vectors, and that contrastive learningusing Boost-RS outperforms attribute concatenation and multi-label learning. Wealso show that Boost-RS outperforms similarity-based models. Ablation studiesand visualization of learned representations highlight the importance of usingcontrastive learning on some of the auxiliary data in boosting the embeddingvectors.

Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction