Ensemble decision of local similarity indices on the biological network for disease related gene prediction
Mustafa Özgür Cingiz
TL;DR
This paper uses an ensemble method to predict disease-related genes in a protein network, showing improved accuracy for cancers like breast cancer.
Contribution
The study introduces a simple majority voting ensemble method to enhance disease-related gene prediction in PPI networks.
Findings
The ensemble method (SMV) outperformed individual local similarity indices in predicting disease-related genes for breast cancer.
Increasing the number of top-ranked genes improved the performance of the SMV method.
Adamic-Adar and Resource Allocation Index methods showed good performance across various cancers.
Abstract
Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsBioinformatics and Genomic Networks · Computational Drug Discovery Methods · Gene expression and cancer classification