Virus–human protein–protein interaction prediction using Bayesian matrix factorization and projection techniques

• Autorzy: Nourani E. , Khunjush F. , Sevilgen F. E.

Identyfikatory

DOI

10.1016/j.bbe.2018.04.006

• Języki publikacji: EN

Abstrakty

EN

Pathogens infect host organisms by exploiting host cellular mechanisms and evading host defence mechanisms through molecular pathogen–host interactions (PHIs). Discovering new interactions between pathogen and human proteins is very crucial in understanding the infection mechanisms. By analysing interaction networks, the interactions responsible for infectious diseases can be detected and new drugs disabling these interactions can be delivered. In this paper, we propose a method based on Bayesian matrix factorization for predicting PHIs along with a projection-based technique and combine the results by employing an ensemble method. Furthermore, two features, target similarity and attacker similarity, are utilized for the first time in the literature for PHI prediction. The advantages of the proposed methods are two folds. Firstly, they relieve the need for negative samples which is significant since there is no available dataset providing negative samples for most of the pathogenic systems. Secondly, the experiments demonstrate that the proposed approach outperforms state-of-the-art methods; roughly 20% of top 50 predictions are among recently validated interactions. So, the search space for wet-lab experiments to obtain validated interactions can be considerably narrowed down from a huge number of possible interactions.

Słowa kluczowe

EN

bioinformatics; protein-protein interaction; pathogen host interaction; interaction prediction; kernelized projection

PL

bioinformatyka; interakcja białek; interakcja gospodarz-patogen; prognozowanie interakcji

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 3
• Strony: 574--585
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Nourani E.

• Department of Information Technology, Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Kilometere 35, Tabriz/Azarshahr Road, Tabriz, Iran

autor

Khunjush F.

• Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran; School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

autor

Sevilgen F. E.

• Department of Computer Engineering, Gebze Technical University, Kocaeli, Turkey

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).