ArticleBMC pulmonary medicine2021
Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks.
Article in BMC pulmonary medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application.Statistics in medicine · 2025Article
- Partial correlation network analysis identifies coordinated gene expression within a regional cluster of COPD genome-wide association signals.PLoS computational biology · 2024Article
- Opportunities to improve asthma and COPD prevention and care: insights from the patient journey obtained through focus groups.BMJ open quality · 2023Article
- Eleven Crucial Pesticides Appear to Regulate Key Genes That Link MPTP Mechanism to Cause Parkinson's Disease through the Selective Degeneration of Dopamine Neurons.Brain sciences · 2023Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment.
methodsIn this paper, a novel method was proposed to Prioritize genes using Expression information in Protein-protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order.
resultsFurther analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD.
conclusionsThe proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data.
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