# Distributed Representations for Biological Sequence Analysis

arXiv: https://arxiv.org/abs/1608.05949 · HTML: https://mlsanity.com/paper/1608.05949/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding](https://mlsanity.com/paper/2206.02096/) (2022)
- [Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches](https://mlsanity.com/paper/2202.13884/) (2022)
- [Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics](https://mlsanity.com/paper/2506.02212/) (2025)
- [Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis](https://mlsanity.com/paper/2510.01118/) (2025)

## Abstract (arXiv)

Biological sequence comparison is a key step in inferring the relatedness of various organisms and the functional similarity of their components. Thanks to the Next Generation Sequencing efforts, an abundance of sequence data is now available to be processed for a range of bioinformatics applications. Embedding a biological sequence over a nucleotide or amino acid alphabet in a lower dimensional vector space makes the data more amenable for use by current machine learning tools, provided the quality of embedding is high and it captures the most meaningful information of the original sequences. Motivated by recent advances in the text document embedding literature, we present a new method, called seq2vec, to represent a complete biological sequence in an Euclidean space. The new representation has the potential to capture the contextual information of the original sequence necessary for sequence comparison tasks. We test our embeddings with protein sequence classification and retrieval tasks and demonstrate encouraging outcomes.