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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper presents the Ensemble Nucleotide Byte-level Encoder-Decoder(ENBED) foundation model, analyzing DNA sequences at byte-level precision withan encoder-decoder Transformer architecture. ENBED uses a sub-quadraticimplementation of attention to develop an efficient model capable ofsequence-to-sequence transformations, generalizing previous genomic models withencoder-only or decoder-only architectures. We use Masked Language Modeling topre-train the foundation model using reference genome sequences and apply it inthe following downstream tasks: (1) identification of enhancers, promotors andsplice sites, (2) identification of biological function annotations of genomicsequences, (3) recognition of sequences containing base call mismatches andinsertion/deletion errors, an advantage over tokenization schemes involvingmultiple base pairs, which lose the ability to analyze with byte-levelprecision, and (4) generating mutations of the Influenza virus using theencoder-decoder architecture and validating them against real-worldobservations. In each of these tasks, we demonstrate significant improvement ascompared to the existing state-of-the-art results.

Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision