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Inferring Genome-Wide Interaction Networks

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

Abstract

The inference of gene regulatory networks is an important process that contributes to a better understanding of biological and biomedical problems. These networks aim to capture the causal molecular interactions of biological processes and provide valuable information about normal cell physiology. In this book chapter, we introduce GNI methods, namely C3NET, RN, ARACNE, CLR, and MRNET and describe their components and working mechanisms. We present a comparison of the performance of these algorithms using the results of our previously published studies. According to the study results, which were obtained from simulated as well as expression data sets, the inference algorithm C3NET provides consistently better results than the other widely used methods.

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Author information

Authors and Affiliations

  1. Biomedical Engineering, Bahcesehir University, Istanbul, Turkey

    Gökmen Altay

  2. Software Engineering, Bahcesehir University, Istanbul, Turkey

    Onur Mendi

  3. Faculty of Medicine, Istanbul Bilim University, Istanbul, Turkey

    Onur Mendi

Authors
  1. Gökmen Altay
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  2. Onur Mendi
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Corresponding author

Correspondence to Gökmen Altay .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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© 2017 Springer Science+Business Media New York

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Altay, G., Mendi, O. (2017). Inferring Genome-Wide Interaction Networks. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_6

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_6

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6611-0

  • Online ISBN: 978-1-4939-6613-4

  • eBook Packages: Springer Protocols

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