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

Multidimensional scaling of gene sequence data has long played a vital rolein analysing gene sequence data to identify clusters and patterns. However thecomputation complexities and memory requirements of state-of-the-artdimensional scaling algorithms make it infeasible to scale to large datasets.In this paper we present an autoencoder-based dimensional reduction model whichcan easily scale to datasets containing millions of gene sequences, whileattaining results comparable to state-of-the-art MDS algorithms with minimalresource requirements. The model also supports out-of-sample data points with a99.5%+ accuracy based on our experiments. The proposed model is evaluatedagainst DAMDS with a real world fungi gene sequence dataset. The presentedresults showcase the effectiveness of the autoencoder-based dimension reductionmodel and its advantages.

Multidimensional Scaling for Gene Sequence Data with Autoencoders