Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
Year: 2023
Type: preprint
Abstract: The rapid expansion of genomic sequence data calls for new methods to achieve robust sequence representations. Existing techniques often neglect intricate structural details, emphasizing mainly contextual information. To address this, we developed k-mer embeddings that merge contextual and structural string information by enhancing De Bruijn graphs with structural similarity connections. Subsequently, we crafted a self-supervised method based on Contrastive Learning that employs a heterogeneous Graph Convolutional Network encoder and constructs positive pairs based on node similarities. Our embeddings consistently outperform prior techniques for Edit Distance Approximation and Closest String Retrieval tasks.
Source: arXiv (Cornell University)
Authors: Kapuśniak, Kacper, Burger, Manuel, Rätsch, Gunnar, Joudaki, Amir
Institution: ETH Zurich
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4389500840