Neural Distance Embeddings for Biological Sequences

TL;DR

NeuroSEED introduces a novel hyperbolic embedding framework for biological sequences, effectively capturing hierarchical relationships and improving accuracy and efficiency over existing methods.

Contribution

The paper presents NeuroSEED, a new geometric embedding framework that models biological sequence evolution more accurately using hyperbolic space, outperforming traditional Euclidean approaches.

Findings

22% average reduction in embedding RMSE with hyperbolic space

Significant accuracy improvements on bioinformatics tasks

Up to 30x faster hierarchical clustering methods

Abstract

The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research. However, popular machine learning approaches, based on continuous Euclidean spaces, have struggled with the discrete combinatorial formulation of the edit distance that models evolution and the hierarchical relationship that characterises real-world datasets. We present Neural Distance Embeddings (NeuroSEED), a general framework to embed sequences in geometric vector spaces, and illustrate the effectiveness of the hyperbolic space that captures the hierarchical structure and provides an average 22% reduction in embedding RMSE against the best competing geometry. The capacity of the framework and the significance of these improvements are then demonstrated devising supervised and unsupervised NeuroSEED approaches to multiple core tasks in bioinformatics. Benchmarked with common baselines, the proposed approaches display significant accuracy and/or runtime improvements on real-world datasets. As an example for hierarchical clustering, the proposed pretrained and from-scratch methods match the quality of competing baselines with 30x and 15x runtime reduction, respectively.