Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical–genetic interactions from nonessential gene profiles in yeast.
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All data, standards, BIONIC yeast features and chemical–genetic interaction data are available in the following Figshare repository: https://figshare.com/projects/BIONIC_Biological_Network_Integration_using_Convolutions/122585. There are no restrictions on the data. Source data are provided with this paper.
The BIONIC code is available at https://github.com/bowang-lab/BIONIC77. Code to reproduce the main figure analyses (Figs. 2–6) is available at https://github.com/duncster94/BIONIC-analyses78 and a library implementing the coannotation prediction, module detection and gene function prediction evaluations is available at https://github.com/duncster94/BIONIC-evals79. The BIONIC integrated yeast features (PEG features) can be explored at https://bionicviz.com.
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We thank B. Andrews, M. Costanzo and C. Myers for their insightful comments. We also thank M. Fey for adding important features to the PyTorch Geometric library for us. This work was supported by NRNB (US National Institutes of Health, National Center for Research Resources grant number P41 GM103504). Funding for continued development and maintenance of Cytoscape is provided by the US National Human Genome Research Institute under award number HG009979. This work was also supported by the Canadian Institutes of Health Research Foundation grant number FDN-143264, US National Institutes of Health grant number R01HG005853 and joint funding by Genome Canada (OGI-163) and the Ministry of Economic Development, Job Creation and Trade, under the program Bioinformatics and Computational Biology. This work was supported by the National Research Council of Canada through the AI for Design program. This work was supported by CIFAR AI Chair programs. This work was also supported by JSPS KAKENHI grant numbers JP15H04483 (C.B. and Y.O.), JP17H06411 (C.B. and Y.Y.), JP18K14351 (K.I.-N.), JP19H03205 (Y.O.), JP20K07487 (D.Y.) and a RIKEN Foreign Postdoctoral Fellowship (S.C.L.). This research was enabled in part by support provided by SciNet and the Digital Research Alliance of Canada.
The authors declare no competing interests.
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Extended Data Fig. 1 Detailed view of individual BIONIC network encoder.
A more detailed view of an individual network encoder, including residual connections. A network specific graph convolutional network is used to encode the input network for increasing neighborhood sizes. The first GCN in the sequence learns features for a given node based on the node’s immediate neighborhood (1st order features). The next GCN learns features based on the node’s second order neighborhood (2nd order features), and so on. The node feature matrices learned by each GCN pass are summed together to create the final learned, network-specific features. Summing the outputs of the various GCNs in this way creates residual connections, allowing features from multiple neighborhood sizes to generate the final learned features, rather than just the final neighborhood size. This figure shows three GCN layers, but BIONIC uses the same pattern of connections for any number of GCN layers. Note that the GCN layers for a given encoder share their weights, so in effect, there is a single GCN layer for each encoder.
Extended Data Fig. 2 Comparison of individual network features produced by BIONIC.
A comparison of individual networks (denoted ‘Net’), their corresponding features encoded using the unsupervised BIONIC (denoted ‘BIONIC’), as well as the BIONIC integration of these networks (denoted ‘GI+COEX+PPI BIONIC’). BP = Biological Processes, GI = Genetic Interaction, COEX = Co-expression, PPI = Protein-protein Interaction. These are the same networks and evaluations used in Fig. 2. Data are presented as mean values. Error bars indicate the 95% confidence interval for n = 10 independent samples.
Extended Data Fig. 3 Dynamics of BIONIC feature space through training.
Comparison of pairwise gene similarities (cosine similarity in the case of BIONIC, direct binary adjacency in the case of the network), as defined by IntAct Complexes for known co-complex relationships (positive pairs) and no co-complex relationships (negative pairs), between a yeast PPI network (as used in the Fig. 2 analyses) and the unsupervised BIONIC features produced from this network. The BIONIC similarities are shown throughout the training process (epochs), whereas the input network is constant so its pairwise similarities do not change. ‘Network’ denotes the input PPI network, ‘BIONIC’ denotes the features learned from this network using BIONIC.
Extended Data Fig. 4 Coverage of BIONIC and input network captured modules.
Coverage of functional gene modules by individual networks and the unsupervised BIONIC integration of these networks (denoted BIONIC), as determined by a parameter optimized module detection analysis where the clustering parameters were optimized for each module individually. The number of captured modules is reported for a range of overlap scores (Jaccard threshold). Higher threshold indicates greater correspondence between the clusters obtained from the dataset and their respective modules given by the standard. PPI = protein-protein interaction. These are the same networks and BIONIC features as Fig. 2.
Extended Data Fig. 5 Captured modules comparison for BIONIC and input networks for optimal clustering parameters.
Known protein complexes (as defined by the IntAct standard) captured by individual networks and the unsupervised BIONIC integration of these networks (denoted BIONIC). Hierarchical clustering was performed on the datasets and resulting clusters were compared to known IntAct complexes and scored for set overlap using the Jaccard score (ranging from 0 to 1). The clustering algorithm parameters were optimized for each module individually, unlike the analysis in Fig. 2 where the clustering parameters were optimized for the standard as a whole. Each point is a protein complex, as in Fig. 2c. The dashed line indicates instances where the given data sets achieve the same score for a given module. Histograms indicate the distribution of overlap (Jaccard) scores for the given dataset, and the labelled dashed line indicates the mean of this distribution. The individual modules shown here as well as for the KEGG Pathways and IntAct Complexes module standards can be found in Supplementary Data File 4. The LSM2-7 complex is indicated by the arrows. PPI = protein-protein interaction. This analysis uses the same networks and BiONIC features as Fig. 2.
Extended Data Fig. 6 Interpretability of BIONIC feature space.
Co-annotation evaluations of the unsupervised BIONIC features subset to different clusters of feature dimensions (denoted ‘Cluster’). The number of feature dimensions for each cluster is given in parenthesis. The performance of the original BIONIC features (denoted BIONIC (512)) is also displayed. Data are presented as mean values. Bars indicate 95% confidence interval for n = 10 independent samples.
Extended Data Fig. 7 Integration method performance for yeast-two-hybrid network inputs.
Performance comparison of 5 yeast-two-hybrid network integrations across functional standards, evaluation types and unsupervised integration methods. Data are presented as mean values. Bars indicate 95% confidence interval for n = 10 independent samples. BP = Biological Process, multi-n2v = multi-node2vec.
Extended Data Fig. 8 Effects of label poisoning on BIONIC semi-supervised and unsupervised performance.
Semi-supervised BIONIC comparisons. a) A label poisoning experiment, where progressively more permutation noise is added to the label sets the semi-supervised BIONIC is trained on. ‘Noise’ indicates the proportion of permutation noise applied (multiply by 100 for percentages). Data are presented as mean values. Bars indicate 95% confidence interval for n = 10 independent samples. b) UMAP plots comparing the embedding space of the TFIID complex and the 100 nearest neighbors of this complex for unsupervised and semi-supervised BIONIC over a range of label noise values. SS = average silhouette score of TFIID members.
Extended Data Fig. 9 Computational scalability of BIONIC.
Graphics processing unit (GPU) memory usage in gigabytes (left) and average wall clock epoch time in minutes (right) for a range of network sizes and number of networks. GB = gigabyte, min = minutes. Gray squares indicate a scenario where BIONIC exceeded the maximum memory of the GPU and failed to complete. The experiments were run on a Titan Xp GPU with a 2.4 GHz Intel Xeon CPU and 32 GB of system memory.
Extended Data Fig. 10 β-1,6-glucan levels in yeast strains.
The amount of glucan per cell was calculated using pustulan as a standard. Data are presented as mean values. Error bars indicate standard deviation for n = 3 biologically independent samples. kre6Δ compared to wild type p-value = 0.01473, Jervine compares to wild type p-value = 0.01520. * Significant difference (p-value < 0.05 after Bonferroni correction, Welch’s one-sided t-test).
Supplementary Figs. 1–7 and Notes 1–4.
Supplementary Data 1
Hyperparameter optimization results. Hyperparameter optimization results across integration methods integrating three S. pombe networks. The chosen (best) hyperparameter combinations for each method are highlighted.
Supplementary Data 2
Integrated network details. Publication, gene count, edge count and experimental type for each yeast network and each human network used in Figs. 2–6. Rows in yellow indicate the three yeast networks used in Figs. 2–4 and 6 integrations.
Supplementary Data 3
Evaluation standards details. Gene count, coannotation count, module count and class count details for each standard used in the Figs. 2–5 evaluations.
Supplementary Data 4
Module detection results. Overlap of standard-optimized clusters obtained from the Fig. 2c module detection analysis for networks as well as integration methods. Module standards are IntAct complexes, KEGG pathways and GO Biological Processes.
Supplementary Data 5
Extended Data Figs. 4–5 Module detection results. Overlap of known per-module-optimized clusters obtained from the Figs. 2 and 3 input networks and integration methods, with IntAct complex, KEGG pathway and GO Biological Process modules.
Supplementary Data 6
50 compound TS Aalele screen results. Files containing the TS allele chemical–genetic scores and IQR scores, screened against 50 compounds (at multiple concentrations) that were selected by BIONIC.
Supplementary Data 7
Essential gene compound sensitivity predictions. Essential yeast gene compound sensitivity predictions for 50 selected compounds using BIONIC.
Supplementary Data 8
Integrated BIONIC features. Learned BIONIC features from yeast networks (protein–protein interaction, coexpression and genetic interaction) integrated and used in Figs. 2, 3 and 6.
Supplementary Data 9
Evaluation standards. Yeast evaluation standards for coannotation prediction, module detection and gene function prediction used in Figs. 2a, 3a, 4 and 5a as well as the human coannotation standard used in Fig. 5b.
Source Data Fig. 2
Statistical source data.
Source Data Fig. 3
Statistical source data.
Source Data Fig. 4
Statistical source data.
Source Data Fig. 5
Statistical source data.
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Forster, D.T., Li, S.C., Yashiroda, Y. et al. BIONIC: biological network integration using convolutions. Nat Methods 19, 1250–1261 (2022). https://doi.org/10.1038/s41592-022-01616-x