Prediction of host-pathogen protein interactions by extended network model
Yazarlar (3)
Kocaeli Üniversitesi, Türkiye
Prof. Dr. Murat GÖK
Yalova Üniversitesi, Türkiye
Yabancı Kurumlar, Amerika Birleşik Devletleri
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Turkish Journal of Biology |
| Dergi ISSN | 1300-0152 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2021 |
| Cilt No | 45 |
| Sayı | 2 |
| Sayfalar | 138 / 148 |
| DOI Numarası | 10.3906/biy-2009-4 |
| Makale Linki | http://dx.doi.org/10.3906/biy-2009-4 |
| Özet |
| Knowledge of the pathogen-host interactions between the species is essentialin order to develop a solution strategy againstinfectious diseases. In vitro methods take extended periods of time to detect interactions and provide very few of the possible interactionpairs. Hence, modelling interactions between proteins has necessitated the development of computational methods. The main scope ofthis paper is integrating the known protein interactions between thehost and pathogen organisms to improve the prediction success rateof unknown pathogen-host interactions. Thus, the truepositive rate of the predictions was expected to increase.In order to perform thisstudy 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 afterintegrating the known protein interactions within each organism. The effect of merging the networks of pathogen-host interactions ofdifferent 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 andpathogen interactions yields better performance consistently in almost all experiments. |
| Anahtar Kelimeler |
| machine learning | Infectious diseases | host-pathogen interactions | protein-protein interactions | protein networks | bioinformatics |