Prediction of Synthetic Lethality in Escherichia coli Based on Feature Engineering through Graph Embedding

Abstract

Synthetic lethality (SL) is a genetic interaction that refers to the phenomenon of cell death caused by the simultaneous inactivation of two non-lethal genes. Due to high-cost constraints and time consumption of experimental screening, computational prediction methods have become the main research tool. Currently, methods based on machine learning have been widely used in SL research, and discovering effective features to enhance the accuracy of predictions remains the key challenge to overcome in current research. We propose an SL prediction method based on graph embedding. First, we transformed five types of raw omics data into graph structures to capture the complex associations among genes. Then, using the graph embedding technique, we extracted feature information for each gene and constructed the feature representation of SL pairs by mathematical operations. Finally, different from GNN, which infers a single graph, we used the machine learning classifiers to discriminate positive and negative samples. Our method achieved better AUC than GNN-based baseline methods. Overall, this study firstly proposed a prediction model for Escherichia coli (E. coli) SLs that integrates the advantages of graph embedding techniques and classifier ensembles, which significantly improves the accuracy and reliability of prediction, and also provides new perspectives and methods for this field.

Keywords

sythetic lethality / Escherichia coli / machine learning / graph embedding

Author Contributions

Q.X. and Y.F.: Conceptualization, methodology; Y.S.: validation; H.G.: formal analysis; Q.X.: data curation, writing—original draft preparation; Y.F.: writing—review and editing; X.C.: visualization; H.S. and J.F.: supervision; F.G.: project administration; F.G.: funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (grant no. 32370696).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the datasets and source codes implementing our method are uploaded to https://github.com/Christal6/ECSLPredict/ (accessed on 3 March 2025).

Acknowledgments

We thank Hongtu Cui and Xiang Lian at the Guo lab for their useful discussions and valuable suggestions.

Conflicts of Interest

The author declares no conflict of interest.

Use of AI and AI-Assisted Technologies

No AI tools were utilized for this paper.

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