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Ess-NEXG: Predict Essential Proteins by Constructing a Weighted Protein Interaction Network Based on Node Embedding and XGBoost

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Bioinformatics Research and Applications (ISBRA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12304))

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Abstract

Essential proteins are indispensable in the development of organisms and cells. Identification of essential proteins lays the foundation for the discovery of drug targets and understanding of protein functions. Traditional biological experiments are expensive and time-consuming. Considering the limitations of biological experiments, many computational methods have been proposed to identify essential proteins. However, lots of noises in the protein-protein interaction (PPI) networks hamper the task of essential protein prediction. To reduce the effects of these noises, constructing a reliable PPI network by introducing other useful biological information to improve the performance of the prediction task is necessary. In this paper, we propose a model called Ess-NEXG which integrates RNA-Seq data, subcellular localization information, and orthologous information, for the prediction of essential proteins. In Ess-NEXG, we construct a reliable weighted network by using these data. Then we use the node2vec technique to capture the topological features of proteins in the constructed weighted PPI network. Last, the extracted features of proteins are put into a machine learning classifier to perform the prediction task. The experimental results show that Ess-NEXG outperforms other computational methods.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grants (No. 61832019), the 111 Project (No. B18059), Hunan Provincial Science and Technology Program (2018WK4001).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, People’s Republic of China

    Nian Wang, Min Zeng, Jiashuai Zhang, Yiming Li & Min Li

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  1. Nian Wang
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  2. Min Zeng
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  3. Jiashuai Zhang
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  4. Yiming Li
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  5. Min Li
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Corresponding author

Correspondence to Min Li .

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Editors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  2. University of Connecticut, Storrs Mansfield, CT, USA

    Ion Mandoiu

  3. Florida International University, Miami, FL, USA

    Giri Narasimhan

  4. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  5. University of North Texas, Denton, TX, USA

    Xuan Guo

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Wang, N., Zeng, M., Zhang, J., Li, Y., Li, M. (2020). Ess-NEXG: Predict Essential Proteins by Constructing a Weighted Protein Interaction Network Based on Node Embedding and XGBoost. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-57821-3_9

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