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Functional network construction in Arabidopsis using rule-based machine learning on large-scale data sets

Bassel, George W.; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J.; Barcardit, Jaume

Authors

George W. Bassel

Enrico Glaab

Julietta Marquez

Michael J. Holdsworth

Jaume Barcardit



Abstract

The meta-analysis of large-scale postgenomics data sets within public databases promises to provide important novel biological knowledge. Statistical approaches including correlation analyses in coexpression studies of gene expression have emerged as tools to elucidate gene function using these data sets. Here, we present a powerful and novel alternative methodology to computationally identify functional relationships between genes from microarray data sets using rule-based machine learning. This approach, termed “coprediction,” is based on the collective ability of groups of genes co-occurring within rules to accurately predict the developmental outcome of a biological system. We demonstrate the utility of coprediction as a powerful analytical tool using publicly available microarray data generated exclusively from Arabidopsis thaliana seeds to compute a functional gene interaction network, termed Seed Co-Prediction Network (SCoPNet). SCoPNet predicts functional associations between genes acting in the same developmental and signal transduction pathways irrespective of the similarity in their respective gene expression patterns. Using SCoPNet, we identified four novel regulators of seed germination (ALTERED SEED GERMINATION5, 6, 7, and 8), and predicted interactions at the level of transcript abundance between these novel and previously described factors influencing Arabidopsis seed germination. An online Web tool to query SCoPNet has been developed as a community resource to dissect seed biology and is available at http://www.vseed.nottingham.ac.uk/.

Citation

Bassel, G. W., Glaab, E., Marquez, J., Holdsworth, M. J., & Barcardit, J. (2011). Functional network construction in Arabidopsis using rule-based machine learning on large-scale data sets. Plant Cell, 23(9), doi:10.1105/tpc.111.088153

Journal Article Type Article
Publication Date Sep 1, 2011
Deposit Date Apr 17, 2014
Publicly Available Date Apr 17, 2014
Journal Plant Cell
Print ISSN 1040-4651
Electronic ISSN 1040-4651
Publisher American Society of Plant Biologists
Peer Reviewed Peer Reviewed
Volume 23
Issue 9
DOI https://doi.org/10.1105/tpc.111.088153
Public URL http://eprints.nottingham.ac.uk/id/eprint/2434
Publisher URL http://www.plantcell.org/content/early/2011/09/05/tpc.111.088153.short
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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