GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning

TL;DR

GlycanML establishes a comprehensive benchmark for glycan property prediction using diverse tasks, representations, and multi-task learning, advancing machine learning applications in glycan research.

Contribution

This work introduces the first standardized benchmark for glycan property prediction, including diverse tasks, representations, and multi-task learning frameworks.

Findings

Multi-relational GNNs outperform other models.

Multi-task learning enhances prediction accuracy.

Sequence and graph representations are both effective.

Abstract

Glycans are basic biomolecules and perform essential functions within living organisms. The rapid increase of functional glycan data provides a good opportunity for machine learning solutions to glycan understanding. However, there still lacks a standard machine learning benchmark for glycan property and function prediction. In this work, we fill this blank by building a comprehensive benchmark for Glycan Machine Learning (GlycanML). The GlycanML benchmark consists of diverse types of tasks including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction. Glycans can be represented by both sequences and graphs in GlycanML, which enables us to extensively evaluate sequence-based models and graph neural networks (GNNs) on benchmark tasks. Furthermore, by concurrently performing eight glycan taxonomy prediction tasks, we introduce the GlycanML-MTL testbed for multi-task learning (MTL) algorithms. Also, we evaluate how taxonomy prediction can boost other three function prediction tasks by MTL. Experimental results show the superiority of modeling glycans with multi-relational GNNs, and suitable MTL methods can further boost model performance. We provide all datasets and source codes at https://github.com/GlycanML/GlycanML and maintain a leaderboard at https://GlycanML.github.io/project