On the robustness of the biological correlation network model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Recent progress in high-throughput technology has resulted in a significant data overload. Determining how to obtain valuable knowledge from such massive raw data has become one of the most challenging issues in biomedical research. As a result, bioinformatics researchers continue to look for advanced data analysis tools to analysis and mine the available data. Correlation network models obtained from various biological assays, such as those measuring gene expression levels, are a powerful method for representing correlated expression. Although correlation does not always imply causation, the correlation network has been shown to be effective in identifying elements of interest in various bioinformatics applications. While these models have found success, little to no investigation has been made into the robustness of relationships in the correlation network with regard to vulnerability of the model according to manipulation of sample values. Particularly, reservations about the correlation network model stem from a lack of testing on the reliability of the model. In this work, we probe the robustness of the model by manipulating samples to create six different expression networks and find a slight inverse relationship between sample count and network size/density. When samples are iteratively removed during model creation, the results suggest that network edges may or may not remain within the statistical parameters of the model, suggesting that there is room for improvement in the filtering of these networks. A cursory investigation into a secondary robustness threshold using these measures confirms the existence of a positive relationship between sample size and edge robustness. This work represents an important step toward better understanding of the critical noise versus signal issue in the correlation network model.

Original languageEnglish (US)
Title of host publicationBIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014
PublisherSciTePress
Pages186-195
Number of pages10
ISBN (Print)9789897580123
DOIs
StatePublished - 2014
Event5th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014 - Angers, Loire Valley, France
Duration: Mar 3 2014Mar 6 2014

Publication series

NameBIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014

Conference

Conference5th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014
CountryFrance
CityAngers, Loire Valley
Period3/3/143/6/14

Keywords

  • Correlation Networks
  • Network Stability

ASJC Scopus subject areas

  • Biomedical Engineering
  • Modeling and Simulation

Fingerprint Dive into the research topics of 'On the robustness of the biological correlation network model'. Together they form a unique fingerprint.

Cite this