Protein complex detection from protein-protein interaction networks with machine learning methods
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pamukkale Univ
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Understanding Protein - Protein interaction networks, which show the interactions between proteins involved in tasks that are very important for our organisms such as structural support, storage, signal transduction and defence, provides a better understanding of cellular processes. One of the important studies carried out for this purpose is to try to detect protein complexes from protein - protein interaction networks. Supervised and unsupervised machine learning methods were used to detect protein complexes. It is known that the machine learning methods used produce better performance when more than one method is used together. Based on this knowledge, a method that detects protein complexes from protein-protein interaction networks is proposed in this study. The method first weights protein-protein interaction networks using biological and topological properties of proteins. Then it estimates local and global protein complex core. Then it builds a protein complex detection model using the structural modularity of proteins and the voting regression model. We predict that XGB regression, gaussian process regression, catboost regression and histogram -based gradient boosting regression supervised learning methods can achieve more successful results when used together in the voting regression model. When we compare the success of the model with other models, it has shown the best performance many times among the compared models.
Açıklama
Anahtar Kelimeler
Protein-Protein interaction networks, Protein complex detection, Machine learning, Voting regression, Bioinformatics, Network embedding
Kaynak
Pamukkale University Journal of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri Dergisi
WoS Q Değeri
Q3
Scopus Q Değeri
Cilt
30
Sayı
3
