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Open Access Highly Accessed Research article

Spice: discovery of phenotype-determining component interplays

Zhengzhang Chen12, Kanchana Padmanabhan12, Andrea M Rocha3, Yekaterina Shpanskaya4, James R Mihelcic3, Kathleen Scott5 and Nagiza F Samatova12*

Author affiliations

1 Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA

2 Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

3 Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA

4 Trinity College of Arts and Sciences, Duke University, Durham, NC 27708, USA

5 Department of Integrative Biology, University of South Florida, Tampa, FL 33620, USA

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Citation and License

BMC Systems Biology 2012, 6:40  doi:10.1186/1752-0509-6-40

Published: 14 May 2012

Abstract

Background

A latent behavior of a biological cell is complex. Deriving the underlying simplicity, or the fundamental rules governing this behavior has been the Holy Grail of systems biology. Data-driven prediction of the system components and their component interplays that are responsible for the target system’s phenotype is a key and challenging step in this endeavor.

Results

The proposed approach, which we call System Phenotype-related Interplaying Components Enumerator (SPICE), iteratively enumerates statistically significant system components that are hypothesized (1) to play an important role in defining the specificity of the target system’s phenotype(s); (2) to exhibit a functionally coherent behavior, namely, act in a coordinated manner to perform the phenotype-specific function; and (3) to improve the predictive skill of the system’s phenotype(s) when used collectively in the ensemble of predictive models. SPICE can be applied to both instance-based data and network-based data. When validated, SPICE effectively identified system components related to three target phenotypes: biohydrogen production, motility, and cancer. Manual results curation agreed with the known phenotype-related system components reported in literature. Additionally, using the identified system components as discriminatory features improved the prediction accuracy by 10% on the phenotype-classification task when compared to a number of state-of-the-art methods applied to eight benchmark microarray data sets.

Conclusion

We formulate a problem—enumeration of phenotype-determining system component interplays—and propose an effective methodology (SPICE) to address this problem. SPICE improved identification of cancer-related groups of genes from various microarray data sets and detected groups of genes associated with microbial biohydrogen production and motility, many of which were reported in literature. SPICE also improved the predictive skill of the system’s phenotype determination compared to individual classifiers and/or other ensemble methods, such as bagging, boosting, random forest, nearest shrunken centroid, and random forest variable selection method.