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Reusability report: Capturing properties of biological objects and their relationships using graph neural networks

Nature Machine Intelligence volume 4, pages 222–226 (2022)Cite this article

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The Original Article was published on 12 April 2021

arising from R. Schulte-Sasse et al. Nature Machine Intelligence https://doi.org/10.1038/s42256-021-00325-y (2021).

Graph neural networks (GNNs)1, especially graph convolutional networks (GCNs)2, have been increasingly used to model complex interactions. A basic idea behind GNNs is that some properties of an object, represented by a node in a graph, are reflected by properties of the objects that it directly and indirectly interacts with, where direct interactions are represented by edges in the graph. In biomedicine, GNNs have been used in a variety of applications, such as predicting protein functions and drug–disease associations3. Schulte-Sasse et al. have recently proposed a new use for GCNs in biomedicine: identifying cancer genes4. Their method, EMOGI (explainable multiomics graph integration), integrates multiomic data by aggregating information over a protein–protein interaction (PPI) network. The integrated information was shown to predict cancer genes better than having multiomic data alone or PPI connections alone.

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Fig. 1: Reproducing the main reported performance results of EMOGI.
Fig. 2: Performance (AUPRC values) of predicting cancer genes using EMOGI but with its PPI network replaced by co-expression network.
Fig. 3: Performance (AUPRC values) of predicting essential genes using EMOGI and baseline methods.
Fig. 4: Comparison of GATs and GCNs in the prediction of cancer genes.

Data availability

The data used in our study are available at https://github.com/kevingroup/emogi-reusability23. All the data used in the original paper by Schulte-Sasse et al. for testing EMOGI are available at http://owww.molgen.mpg.de/~sasse/EMOGI/.

Code availability

The original EMOGI code is available at https://github.com/schulter/EMOGI. Our modified GAT-based version of it is available at https://github.com/kevingroup/emogi-reusability23.

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Acknowledgements

We thank Schulte-Sasse et al. for their help in our running of EMOGI and for sharing their processed data. Q.C. is supported by National Natural Science Foundation of China Youth Program 32100515. K.Y.Y. was supported by Hong Kong Research Grants Council Collaborative Research Funds C4015-20E, C4045-18W, C4057-18E and C7044-19G and General Research Funds 14107420 and 14203119, the Hong Kong Epigenomics Project (EpiHK), and the Chinese University of Hong Kong Young Researcher Award, Outstanding Fellowship and Project Impact Enhancement Fund.

Author information

Author notes

  1. Kevin Y. Yip

    Present address: Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

  2. These authors contributed equally: Chenyang Hong, Qin Cao.

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Chenyang Hong, Qin Cao, Zhenghao Zhang & Kevin Y. Yip

  2. School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Qin Cao & Stephen Kwok-Wing Tsui

  3. Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Stephen Kwok-Wing Tsui & Kevin Y. Yip

  4. Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Kevin Y. Yip

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  1. Chenyang Hong
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Contributions

Q.C. and K.Y.Y. conceived and supervised the project. C.H., Q.C., Z.Z., S.K.T. and K.Y.Y. designed the computational experiments and data analyses. C.H. and Z.Z. prepared the data. C.H. and Q.C. implemented the methods, conducted the experiments and performed data analyses. C.H., Q.C. and K.Y.Y. wrote the manuscript.

Corresponding authors

Correspondence to Qin Cao or Kevin Y. Yip.

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Nature Machine Intelligence thanks Marinka Zitnik for her contribution to the peer review of this work.

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Supplementary Fig. 1 and Tables 1–3.

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Hong, C., Cao, Q., Zhang, Z. et al. Reusability report: Capturing properties of biological objects and their relationships using graph neural networks. Nat Mach Intell 4, 222–226 (2022). https://doi.org/10.1038/s42256-022-00454-y

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