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Related Experiment Video

Updated: Jun 9, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Interpreting embeddings from genome and protein language models.

Luke E May¹, Jacob B White¹, Garrett W Roell¹

¹Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Mānoa, Honolulu, HI 96822, United States.

Biotechnology Advances

|June 7, 2026

Summary

Large language models trained on DNA and protein data yield insights through sequence embeddings. These embeddings offer a powerful, alignment-free method for biological discovery and engineering.

Keywords:

Embeddings Genome language models Protein language models Representation learning

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics
Proteomics

Background:

Advances in natural language processing (NLP) have led to large language models (LLMs) trained on biological sequences.
LLMs, initially for sequence generation, offer valuable intermediate representations (embeddings) for biological insights.

Purpose of the Study:

To highlight the utility of LLM embeddings for biological sequence analysis.
To demonstrate the application of LLM embeddings in genomics and proteomics.

Main Methods:

Training LLMs on DNA and protein sequence databases.
Extracting and analyzing intermediate sequence embeddings from trained LLMs.
Applying embeddings to predict various biological properties.

Main Results:

LLM embeddings successfully predict single nucleotide variant severity, intron/exon boundaries, and anti-CRISPR activity.
Protein LLM embeddings aid in predicting remote homology, protein structure, and protein function.
Embeddings provide a high-resolution, alignment-free framework for biological data.

Conclusions:

LLM embeddings offer a transformative approach to understanding genomes and proteomes.
Embeddings facilitate advancements in enzyme engineering and functional genomics research.