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Title: Improving network inference algorithms using resampling methods

Abstract

Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications.

Authors:
 [1];  [1];  [2];  [1]; ORCiD logo [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Univ. of Virginia, Charlottesville, VA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1503503
Report Number(s):
PNNL-SA-134995
Journal ID: ISSN 1471-2105
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
BMC Bioinformatics
Additional Journal Information:
Journal Volume: 19; Journal Issue: 1; Journal ID: ISSN 1471-2105
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; gene regulatory network inference, random subspace method, resampling, bootstrapping, aggregation

Citation Formats

Colby, Sean M., McClure, Ryan S., Overall, Christopher C., Renslow, Ryan S., and McDermott, Jason E. Improving network inference algorithms using resampling methods. United States: N. p., 2018. Web. doi:10.1186/s12859-018-2402-0.
Colby, Sean M., McClure, Ryan S., Overall, Christopher C., Renslow, Ryan S., & McDermott, Jason E. Improving network inference algorithms using resampling methods. United States. doi:10.1186/s12859-018-2402-0.
Colby, Sean M., McClure, Ryan S., Overall, Christopher C., Renslow, Ryan S., and McDermott, Jason E. Fri . "Improving network inference algorithms using resampling methods". United States. doi:10.1186/s12859-018-2402-0. https://www.osti.gov/servlets/purl/1503503.
@article{osti_1503503,
title = {Improving network inference algorithms using resampling methods},
author = {Colby, Sean M. and McClure, Ryan S. and Overall, Christopher C. and Renslow, Ryan S. and McDermott, Jason E.},
abstractNote = {Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications.},
doi = {10.1186/s12859-018-2402-0},
journal = {BMC Bioinformatics},
issn = {1471-2105},
number = 1,
volume = 19,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
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