Robustness and accuracy of functional modules in integrated network analysis

Bioinformatics
Daniela BeisserTobias Müller

Abstract

High-throughput molecular data provide a wealth of information that can be integrated into network analysis. Several approaches exist that identify functional modules in the context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions. In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one final module containing maximally robust nodes and edges. The resulting consensus module identifies and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exemplified on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia.

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References

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Citations

Mar 22, 2014·Future Medicinal Chemistry·Asfar S Azmi, Ramzi M Mohammad
Oct 17, 2015·Scientific Reports·Bing LiYongyan Wang
Aug 25, 2015·Frontiers in Microbiology·Christian W RemmeleMarcus T Dittrich
Apr 22, 2016·Nucleic Acids Research·Alexey A SergushichevMaxim N Artyomov
May 9, 2014·Molecular & Cellular Proteomics : MCP·Desislava BoyanovaMarcus Dittrich
Aug 20, 2016·F1000Research·Nicolas AlcarazJan Baumbach
Sep 22, 2017·Nucleic Acids Research·Nicolas AlcarazJan Baumbach
Nov 19, 2020·BMC Bioinformatics·Nikita AlexeevAlexey Sergushichev
Mar 30, 2021·Statistical Analysis and Data Mining·Mingmei TianHan Yu

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