Work

Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

DOI: 10.1109/bibm62325.2024.10822779

Authors: Kexuan Xin, Qingyun Wang, Junyu Chen, Pengfei Yu, Huimin Zhao, Heng Ji

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: Microbial Metabolic Engineering and Bioproduction

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, Office of Science, Biological and Environmental Research

Award: CENTER FOR ADVANCED BIOENERGY AND BIOPRODUCTS INNOVATION