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Authors: Kathryn M. Dempsey and Hesham H. Ali

Affiliation: University of Nebraska at Omaha, United States

ISBN: 978-989-758-012-3

Keyword(s): Correlation Networks, Network Stability.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Computational Molecular Systems ; Model Design and Evaluation ; Systems Biology

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 corre lation 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. (More)

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Paper citation in several formats:
M. Dempsey, K. and H. Ali, H. (2014). On the Robustness of the Biological Correlation Network Model.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 186-195. DOI: 10.5220/0004805801860195

@conference{bioinformatics14,
author={Kathryn M. Dempsey. and Hesham H. Ali.},
title={On the Robustness of the Biological Correlation Network Model},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={186-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004805801860195},
isbn={978-989-758-012-3},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - On the Robustness of the Biological Correlation Network Model
SN - 978-989-758-012-3
AU - M. Dempsey, K.
AU - H. Ali, H.
PY - 2014
SP - 186
EP - 195
DO - 10.5220/0004805801860195

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