The integration of biological networks provides crucial support for accurate gene function prediction, a task that aims to assign genes to corresponding functional categories through computational methods. However, existing approaches struggle with multi-source heterogeneous networks due to their limited ability to capture complex nonlinear dependencies. Contrastive learning, which captures data distributions by measuring similarities and dissimilarities between samples, can generate semantically rich feature representations, offering a new approach to address the aforementioned issues. In this work, we propose EPILOGUE, a multi-view graph contrastive learning framework for gene function prediction. By integrating graph neural networks with contrastive learning, EPILOGUE enables the extraction of high-quality, discriminative gene representations for accurate functional annotation. Additionally, protein sequences are used as node features, offering biological information beyond network topology and supporting the learning of comprehensive semantic representations. Experiments on yeast and human datasets from the STRING database demonstrate that EPILOGUE outperforms nine state-of-the-art methods across six evaluation metrics, validating its effectiveness in learning semantically rich representations for gene function annotation.

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Journal Article

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Zhang Y, Bai Y, Guo E, Liao Y, Zhao K, Huang W, Cai H

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