Estimation of the proteomic cancer co-expression sub networks by using association estimators

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the...

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Bibliographic Details
Published in:PLOS ONE Vol. 12; no. 11; p. e0188016
Main Authors: Yamanishi, Yoshihiro, Erdoğan, Cihat, Kurt, Zeyneb, Diri, Banu
Format: Journal Article
Language:English
Published: United States Public Library of Science (PLoS) 16.11.2017
Public Library of Science
Subjects:
Algorithms
Bioinformatics
Biology and Life Sciences
Breast cancer
Cancer
Cell cycle
Computational Biology
Computer and Information Sciences
Computer engineering
Correlation
Correlation analysis
Data integration
Datasets
Diabetes
Disease
Entropy
Estimators
Gene expression
Genes
Genetic aspects
Humans
Inference
Internet
Lung cancer
Medicine
Medicine and Health Sciences
Methods
Molecular interactions
Mutual Information
Neoplasm Proteins
Neoplasms
Network analysis
Networks
Physical Sciences
Physiological aspects
Physiology
Protein expression
Protein interaction
Protein Interaction Maps
Proteins
Proteomics
Publishing industry
Q
R
Research and Analysis Methods
Research Article
Science
Shrinkage
Studies
Turkey
Istanbul Turkey
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0188016