Improving network inference algorithms using resampling methods

Colby, Sean M.; McClure, Ryan S.; Overall, Christopher C.; Renslow, Ryan S.; McDermott, Jason E.

doi:10.1186/s12859-018-2402-0

Title: Improving network inference algorithms using resampling methods

Journal Article · Fri Oct 12 00:00:00 EDT 2018 · BMC Bioinformatics

DOI:https://doi.org/10.1186/s12859-018-2402-0· OSTI ID:1618533

Colby, Sean M.; McClure, Ryan S.; Overall, Christopher C.; Renslow, Ryan S.;

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.

View Journal Article

Cite

Export

Save

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: NA; AC05-76RL01830

OSTI ID:: 1618533

Alternate ID(s):: OSTI ID: 1503503

Report Number(s):: PNNL-SA-134995; 376; PII: 2402

Journal Information:: BMC Bioinformatics, Journal Name: BMC Bioinformatics Vol. 19 Journal Issue: 1; ISSN 1471-2105

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: United Kingdom

Language:: English

Citation Metrics:

Cited by: 2 works

Citation information provided by
Web of Science

References (19)

Coordinated Regulation of Virulence during Systemic Infection of Salmonella enterica Serovar Typhimurium Yoon, Hyunjin; McDermott, Jason E.; Porwollik, Steffen PLoS Pathogens, Vol. 5, Issue 2 https://doi.org/10.1371/journal.ppat.1000306	journal	February 2009
Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients Saris, Christiaan GJ; Horvath, Steve; van Vught, Paul WJ BMC Genomics, Vol. 10, Issue 1 https://doi.org/10.1186/1471-2164-10-405	journal	January 2009
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets Saito, Takaya; Rehmsmeier, Marc PLOS ONE, Vol. 10, Issue 3 https://doi.org/10.1371/journal.pone.0118432	journal	March 2015
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods Huynh-Thu, Vân Anh; Irrthum, Alexandre; Wehenkel, Louis PLoS ONE, Vol. 5, Issue 9 https://doi.org/10.1371/journal.pone.0012776	journal	September 2010
Large scale statistical inference of signaling pathways from RNAi and microarray data Froehlich, Holger; Fellmann, Mark; Sueltmann, Holger BMC Bioinformatics, Vol. 8, Issue 1 https://doi.org/10.1186/1471-2105-8-386	journal	October 2007
The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart Bazil, Jason N.; Stamm, Karl D.; Li, Xing PLoS ONE, Vol. 9, Issue 6 https://doi.org/10.1371/journal.pone.0100842	journal	June 2014
Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction Wang, Jing; Ma, Zihao; Carr, Steven A. Molecular & Cellular Proteomics, Vol. 16, Issue 1 https://doi.org/10.1074/mcp.M116.060301	journal	November 2016
Bagging Statistical Network Inference from Large-Scale Gene Expression Data de Matos Simoes, Ricardo; Emmert-Streib, Frank PLoS ONE, Vol. 7, Issue 3 https://doi.org/10.1371/journal.pone.0033624	journal	March 2012
Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size Guo, Wenbin; Calixto, Cristiane P. G.; Tzioutziou, Nikoleta BMC Systems Biology, Vol. 11, Issue 1 https://doi.org/10.1186/s12918-017-0440-2	journal	June 2017
Co-expression networks: graph properties and topological comparisons Xulvi-Brunet, Ramon; Li, Hongzhe Bioinformatics, Vol. 26, Issue 2 https://doi.org/10.1093/bioinformatics/btp632	journal	November 2009
Revealing strengths and weaknesses of methods for gene network inference Marbach, D.; Prill, R. J.; Schaffter, T. Proceedings of the National Academy of Sciences, Vol. 107, Issue 14 https://doi.org/10.1073/pnas.0913357107	journal	March 2010
Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002 McClure, Ryan S.; Overall, Christopher C.; McDermott, Jason E. Nucleic Acids Research, Vol. 44, Issue 18 https://doi.org/10.1093/nar/gkw737	journal	August 2016
RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond Gama-Castro, Socorro; Salgado, Heladia; Santos-Zavaleta, Alberto Nucleic Acids Research, Vol. 44, Issue D1 https://doi.org/10.1093/nar/gkv1156	journal	November 2015
Bio-molecular cancer prediction with random subspace ensembles of support vector machines Bertoni, Alberto; Folgieri, Raffaella; Valentini, Giorgio Neurocomputing, Vol. 63 https://doi.org/10.1016/j.neucom.2004.07.007	journal	January 2005
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles Faith, Jeremiah J.; Hayete, Boris; Thaden, Joshua T. PLoS Biology, Vol. 5, Issue 1 https://doi.org/10.1371/journal.pbio.0050008	journal	January 2007
Reverse engineering cellular networks Margolin, Adam A.; Wang, Kai; Lim, Wei Keat Nature Protocols, Vol. 1, Issue 2 https://doi.org/10.1038/nprot.2006.106	journal	June 2006
The EcoCyc database: reflecting new knowledge about Escherichia coli K-12 Keseler, Ingrid M.; Mackie, Amanda; Santos-Zavaleta, Alberto Nucleic Acids Research, Vol. 45, Issue D1 https://doi.org/10.1093/nar/gkw1003	journal	November 2016
The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, Issue 8 https://doi.org/10.1109/34.709601	journal	January 1998
Stability Indicators in Network Reconstruction Filosi, Michele; Visintainer, Roberto; Riccadonna, Samantha PLoS ONE, Vol. 9, Issue 2 https://doi.org/10.1371/journal.pone.0089815	journal	February 2014

Similar Records

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Journal Article · Wed Jun 16 00:00:00 EDT 2021 · Solar Energy · OSTI ID:1618533

Nie, Yuhao; Zamzam, Ahmed S.; Brandt, Adam

Accelerated training of bootstrap aggregation-based deep information extraction systems from cancer pathology reports

Journal Article · Thu Oct 01 00:00:00 EDT 2020 · Journal of Biomedical Informatics · OSTI ID:1618533

Yoon, Hong-Jun; Klasky, Hilda B.; Gounley, John P.; +10 more

Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

Journal Article · Sun Aug 31 00:00:00 EDT 2008 · PLoS Computational Biology, 4(8):Art. no. e1000166 · OSTI ID:1618533

Taylor, Ronald C; Acquaah-Mensah, George; Singhal, Mudita; +2 more

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
gene regulatory network inference
random subspace method
resampling
bootstrapping
aggregation

Title: Improving network inference algorithms using resampling methods

Citation Formats

References (19)

Similar Records

Related Subjects