Integrating node embeddings and biological annotations for genes to predict disease-gene associations

• Main Authors: Ata, Sezin Kircali, Ou-Yang, Le, Fang, Yuan, Kwoh, Chee-Keong, Wu, Min, Li, Xiao-Li
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Computer science and engineering; Disease Gene Prediction; Node Embeddings
• Online Access: https://hdl.handle.net/10356/105988; http://hdl.handle.net/10220/48817; http://dx.doi.org/10.1186/s12918-018-0662-y
• Institution: Nanyang Technological University

Background : Predicting disease causative genes (or simply, disease genes) has played critical roles in understanding the genetic basis of human diseases and further providing disease treatment guidelines. While various computational methods have been proposed for disease gene prediction, with the recent increasing availability of biological information for genes, it is highly motivated to leverage these valuable data sources and extract useful information for accurately predicting disease genes. Results : We present an integrative framework called N2VKO to predict disease genes. Firstly, we learn the node embeddings from protein-protein interaction (PPI) network for genes by adapting the well-known representation learning method node2vec. Secondly, we combine the learned node embeddings with various biological annotations as rich feature representation for genes, and subsequently build binary classification models for disease gene prediction. Finally, as the data for disease gene prediction is usually imbalanced (i.e. the number of the causative genes for a specific disease is much less than that of its non-causative genes), we further address this serious data imbalance issue by applying oversampling techniques for imbalance data correction to improve the prediction performance. Comprehensive experiments demonstrate that our proposed N2VKO significantly outperforms four state-of-the-art methods for disease gene prediction across seven diseases. Conclusions : In this study, we show that node embeddings learned from PPI networks work well for disease geneprediction, while integrating node embeddings with other biological annotations further improves the performanceof classification models. Moreover, oversampling techniques for imbalance correction further enhances the prediction performance. In addition, the literature search of predicted disease genes also shows the effectiveness of our proposed N2VKO framework for disease gene prediction.