Method
Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging
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* Corresponding author: Igor Jurisica juris@ai.utoronto.ca
1 Department of Medical Biophysics, University of Toronto, 610 University Avenue, Toronto, M5G 2M9, Canada
2 The Campbell Family Institute for Cancer Research and Ontario Cancer Institute, 101 College Street, TMDT 9-305, Toronto, M5G 1L7, Canada
3 Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, M5S 3G4, Canada
Genome Biology 2010, 11:R13 doi:10.1186/gb-2010-11-2-r13
Published: 3 February 2010Additional files
Additional file 1:
Box-plots showing Support Vector Regression performance of modular subnetworks, regular subnetworks, and genes trained to predict age using wild-type worm data and tested on fer-15 worm data.
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Additional file 2:
Comparing Support Vector Regression performance of modular and regular subnetworks trained to predict age using wild-type worm data and tested on fer-15 worm data.
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Additional file 3:
Significant modular subnetworks identified using a modularity coefficient of β = 250, grown using the data in Golden et al.
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Additional file 4:
Known Caenorhabditis elegans longevity genes.
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Additional file 5:
Putative Gene Ontology (GO) Biological Process (BP) annotations for longevity genes.
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