Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses
Yükleniyor...
Tarih
2022-08-13
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of path-ogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors.
Açıklama
Anahtar Kelimeler
Graph convolutional networks, PHI networks, Protein-protein interaction prediction, Amino acids, Computer viruses, Convolution, Convolutional neural networks, Embeddings, Forecasting, Graph neural networks, Numerical methods, Viruses, Amino acid sequence, Convolutional networks, Graph convolutional network, Human proteins, Interaction prediction, Network-based, PHI network, Protein-protein interactions, Proteins, Bioinformatics, Two-hybrid system techniques, Position weight matrix
Kaynak
Computational Biology and Chemistry
WoS Q Değeri
Q2
Q2
Q2
Scopus Q Değeri
Q2
Cilt
101
Sayı
Künye
Koca, M. B., Nourani, E., Abbasoğlu, F., Karadeniz, İ. & Sevilgen, F. E. (2022). Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses. Computational Biology and Chemistry, 101, 1-14. doi:10.1016/j.compbiolchem.2022.107755
