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Title: Real-world protein particle network reconstruction based on advanced hybrid features
Authors: Gul, Haji
Al-Obeidat, Feras
Moreira, Fernando
Tahir, Muhammad
Amin, Adnan
Keywords: Reconstruction biological network
Protein–protein interaction
Real-world entity relationship prediction
Complex networks
Issue Date: 21-Apr-2022
Publisher: Springer
Citation: Gul, H., Al-Obeidat, F., Moreira, F., Tahir, M., & Amin, A. (2022). Real-world protein particle network reconstruction based on advanced hybrid features. In A. Ullah, S. Anwar, Á. Rocha, & S. Gill (Eds.), Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, (vol. 350, pp. 15-22). Springer. Repositório Institucional UPT.
Abstract: Biological network proteins are key operational particles that substantially and operationally cooperate to bring out cellular progressions. Protein links with some other biological network proteins to accomplish their purposes. Physical collaborations are commonly referred to by the relationships of domain-level. The interaction among proteins and biological network reconstruction can be predicted based on various methods such as social theory, similarity, and topological features. Operational particles of proteins collaboration can be indirect among proteins based on mutual fields, subsequently particles of proteins involved in an identical biological progression be likely to harbor similar fields. To reconstruct the real-world network of proteins particles, some methods need only the notations of proteins domain, and then, it can be utilized to multiple species. A novel method we have introduced will analyze and reconstruct the real-world network of protein particles. The proposed technique works based on protein closeness, algebraic connectivity, and mutual proteins. Our proposed method was practically tested over different data sets and reported the results. Experimental results clearly show that the proposed technique worked best as compared to other state-of-the-art algorithms.
ISBN: 978-981-16-7617-8 (Print)
978-981-16-7618-5 (Online)
Appears in Collections:REMIT - Publicações em Livros de Atas Internacionais / Papers in International Proceedings

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