Prediction of host-pathogen protein interactions by extended network model.


Creative Commons License

Kösesoy İ., Gök M., Kahveci T.

Turkish journal of biology = Turk biyoloji dergisi, cilt.45, ss.138-148, 2021 (SCI-Expanded) identifier identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45
  • Basım Tarihi: 2021
  • Doi Numarası: 10.3906/biy-2009-4
  • Dergi Adı: Turkish journal of biology = Turk biyoloji dergisi
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Veterinary Science Database, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.138-148
  • Anahtar Kelimeler: Infectious diseases, host-pathogen interactions, protein-protein interactions, protein networks, machine learning, bioinformatics, RESOURCE, DATABASE
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Knowledge of the pathogen-host interactions between the species is essentialin order to develop a solution strategy against infectious diseases. In vitro methods take extended periods of time to detect interactions and provide very few of the possible interaction pairs. Hence, modelling interactions between proteins has necessitated the development of computational methods. The main scope of this paper is integrating the known protein interactions between thehost and pathogen organisms to improve the prediction success rate of unknown pathogen-host interactions. Thus, the truepositive rate of the predictions was expected to increase.In order to perform this study extensively, encoding methods and learning algorithms of several proteins were tested. Along with human as the host organism, two different pathogen organisms were used in the experiments. For each combination of protein-encoding and prediction method, both the original prediction algorithms were tested using only pathogen-host interactions and the same methodwas testedagain after integrating the known protein interactions within each organism. The effect of merging the networks of pathogen-host interactions of different species on the prediction performance of state-of-the-art methods was also observed. Successwas measured in terms of Matthews correlation coefficient, precision, recall, F1 score, and accuracy metrics. Empirical results showed that integrating the host and pathogen interactions yields better performance consistently in almost all experiments.