Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query

Author:

Diansyah Mohammad Romano1,Kusuma Wisnu Ananta12ORCID,Annisa Annisa1

Affiliation:

1. Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University. Jl. Meranti Wing 20 Level 5, Kampus IPB Darmaga, Bogor 16680, Indonesia

2. Tropical Biopharmaca Research Center, IPB University. Jl. Taman Kencana No. 3, Bogor 16128, Indonesia

Abstract

Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.

Funder

Ministry of Research, Technology, and Higher Education

Publisher

Institute of Research and Community Services Diponegoro University (LPPM UNDIP)

Subject

General Earth and Planetary Sciences,General Environmental Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Unveiling the anti-obesity potential of Kemuning (Murraya paniculata): A network pharmacology approach;PLOS ONE;2024-08-29

2. Construction and analysis of protein-protein interaction to identify the molecular mechanism in hypertension;THE 2ND INTERNATIONAL CONFERENCE ON NATURAL SCIENCES, MATHEMATICS, APPLICATIONS, RESEARCH, AND TECHNOLOGY (ICON-SMART 2021): Materials Science and Bioinformatics for Medical, Food, and Marine Industries;2023

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