Abstract

Most methods for metagenomic binning rely solely on the local properties of the individual contigs. Because of this, these techniques are unable to take advantage of the connections between contigs as established by the assembly graph. In this paper, we explore Graph Neural Networks (GNNs) to leverage the assembly graph when learning contig representations for metagenomic binning. We applied four different types of GNN architectures, comparing their results on real and synthetic datasets, demonstrating encouraging results and, therefore, a promising research direction to pursue and explore.
Original languageEnglish
Title of host publicationICML Workshop on Computational Biology
Publication date2023
Publication statusPublished - 2023
EventICML'23: International Conference on Machine Learning - Honolulu, United States
Duration: 23 Jul 202329 Jul 2023

Conference

ConferenceICML'23: International Conference on Machine Learning
Country/TerritoryUnited States
CityHonolulu
Period23/07/202329/07/2023

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