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

arXiv: https://arxiv.org/abs/2410.18475 · HTML: https://mlsanity.com/paper/2410.18475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 70 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 30 of 34)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [TuckER: Tensor Factorization for Knowledge Graph Completion](https://mlsanity.com/paper/1901.09590/) (2019)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [SimplE Embedding for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1802.04868/) (2018)
- [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023)
- [Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1611.03954/) (2016)
- [A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs](https://mlsanity.com/paper/2003.07743/) (2020)
- [Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation](https://mlsanity.com/paper/1911.08936/) (2019)
- [Multi-view Knowledge Graph Embedding for Entity Alignment](https://mlsanity.com/paper/1906.02390/) (2019)
- [ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis](https://mlsanity.com/paper/2010.06119/) (2020)
- [PaperRobot: Incremental Draft Generation of Scientific Ideas](https://mlsanity.com/paper/1905.07870/) (2019)
- [ProjE: Embedding Projection for Knowledge Graph Completion](https://mlsanity.com/paper/1611.05425/) (2016)
- [Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer](https://mlsanity.com/paper/2010.03158/) (2020)
- [Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment](https://mlsanity.com/paper/2203.14987/) (2022)
- [The Diminishing Returns of Masked Language Models to Science](https://mlsanity.com/paper/2205.11342/) (2022)
- [Knowing the No-match: Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2106.02248/) (2021)
- [On Multi-Relational Link Prediction with Bilinear Models](https://mlsanity.com/paper/1709.04808/) (2017)
- [Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment](https://mlsanity.com/paper/2104.08804/) (2021)
- [KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation](https://mlsanity.com/paper/2403.14950/) (2024)
- [Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding](https://mlsanity.com/paper/2208.11125/) (2022)
- [Informed Multi-context Entity Alignment](https://mlsanity.com/paper/2201.00304/) (2022)

## Built on by (most cited first, 1 of 1)

- [Canopy: A Heterograph Foundation Model for Metabolic Engineering](https://mlsanity.com/paper/2607.06224/) (2026)

## Abstract (arXiv)

In the rapidly evolving field of metabolic engineering, the quest for efficient and precise gene target identification for metabolite production enhancement presents significant challenges. Traditional approaches, whether knowledge-based or model-based, are notably time-consuming and labor-intensive, due to the vast scale of research literature and the approximation nature of genome-scale metabolic model (GEM) simulations. Therefore, we propose a new task, Gene-Metabolite Association Prediction based on metabolic graphs, to automate the process of candidate gene discovery for a given pair of metabolite and candidate-associated genes, as well as presenting the first benchmark containing 2474 metabolites and 1947 genes of two commonly used microorganisms Saccharomyces cerevisiae (SC) and Issatchenkia orientalis (IO). This task is challenging due to the incompleteness of the metabolic graphs and the heterogeneity among distinct metabolisms. To overcome these limitations, we propose an Interactive Knowledge Transfer mechanism based on Metabolism Graph (IKT4Meta), which improves the association prediction accuracy by integrating the knowledge from different metabolism graphs. First, to build a bridge between two graphs for knowledge transfer, we utilize Pretrained Language Models (PLMs) with external knowledge of genes and metabolites to help generate inter-graph links, significantly alleviating the impact of heterogeneity. Second, we propagate intra-graph links from different metabolic graphs using inter-graph links as anchors. Finally, we conduct the gene-metabolite association prediction based on the enriched metabolism graphs, which integrate the knowledge from multiple microorganisms. Experiments on both types of organisms demonstrate that our proposed methodology outperforms baselines by up to 12.3% across various link prediction frameworks.