GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning
<|reference_start|>GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning: 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<|reference_end|>
@article{xu2024glycanml:,
    title={GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine
  Learning},
    author={Minghao Xu, Yunteng Geng, Yihang Zhang, Ling Yang, Jian Tang, Wentao
  Zhang},
    journal={arXiv preprint arXiv:2405.16206},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16206},
    primaryClass={cs.LG}
}