Prediction of Human- Plasmodium vivax Protein Associations From Heterogeneous Network Structures Based on Machine-Learning Approach

Apichat Suratanee; Teerapong Buaboocha; Kitiporn Plaimas

doi:10.1177/11779322211013350

Prediction of Human- Plasmodium vivax Protein Associations From Heterogeneous Network Structures Based on Machine-Learning Approach

Bioinform Biol Insights. 2021 Jun 16:15:11779322211013350. doi: 10.1177/11779322211013350. eCollection 2021.

Authors

Apichat Suratanee¹, Teerapong Buaboocha^{2

3}, Kitiporn Plaimas^{3

4}

Affiliations

¹ Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
² Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.
³ Omics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.
⁴ Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.

Abstract

Malaria caused by Plasmodium vivax can lead to severe morbidity and death. In addition, resistance has been reported to existing drugs in treating this malaria. Therefore, the identification of new human proteins associated with malaria is urgently needed for the development of additional drugs. In this study, we established an analysis framework to predict human-P. vivax protein associations using network topological profiles from a heterogeneous network structure of human and P. vivax, machine-learning techniques and statistical analysis. Novel associations were predicted and ranked to determine the importance of human proteins associated with malaria. With the best-ranking score, 411 human proteins were identified as promising proteins. Their regulations and functions were statistically analyzed, which led to the identification of proteins involved in the regulation of membrane and vesicle formation, and proteasome complexes as potential targets for the treatment of P. vivax malaria. In conclusion, by integrating related data, our analysis was efficient in identifying potential targets providing an insight into human-parasite protein associations. Furthermore, generalizing this model could allow researchers to gain further insights into other diseases and enhance the field of biomedical science.

Keywords: Network-based method; Plasmodium vivax; host-parasite interaction; human-parasite protein association; machine learning; ranking score; topological profiles.