@inproceedings{d931b19d73a84f8581c74d1625c485f0,
title = "Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production",
abstract = "Identifying gene targets for enhancing metabolite production in metabolic engineering is challenging due to the vast research literature and the approximation in genome-scale metabolic model (GEM) simulations. To address this, we propose the Gene-Metabolite Association Prediction task, which automates gene discovery for given metabolite-gene pairs, accompanied by a benchmark dataset of 2474 metabolites and 1947 genes for Saccharomyces cerevisiae (SC) and Issatchenkia orientalis (IO). This task is complicated by incomplete metabolic graphs and metabolic heterogeneity. We introduce an Interactive Knowledge Transfer mechanism based on Metabolism Graphs (IKT4Meta) to enhance prediction accuracy by integrating cross-metabolism knowledge. Using Pretrained Language Models (PLMs) to generate inter-graph links mitigates heterogeneity issues, while intra-graph links are propagated via these anchors. Gene-metabolite predictions are then performed on the enriched graphs integrating multiple microorganisms' knowledge. Experiments show that IKT4Meta outperforms baselines by up to 12.3% in link prediction.",
keywords = "Gene prediction, association prediction, graph alignment, metabolic network",
author = "Kexuan Xin and Qingyun Wang and Junyu Chen and Pengfei Yu and Huimin Zhao and Heng Ji",
note = "This work is supported by DOE Center for Advanced Bioenergy and Bioproducts Innovation U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DESC0018420. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied of, the National Science Foundation, the U.S. Department of Energy, and the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.; 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 ; Conference date: 03-12-2024 Through 06-12-2024",
year = "2024",
doi = "10.1109/BIBM62325.2024.10822779",
language = "English (US)",
series = "Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "383--388",
editor = "Mario Cannataro and Huiru Zheng and Lin Gao and Jianlin Cheng and {de Miranda}, {Joao Luis} and Ester Zumpano and Xiaohua Hu and Young-Rae Cho and Taesung Park",
booktitle = "Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024",
address = "United States",
}