%0 Journal Article
%T Spice: discovery of phenotype-determining component interplays
%A Zhengzhang Chen
%A Kanchana Padmanabhan
%A Andrea M Rocha
%A Yekaterina Shpanskaya
%A James R Mihelcic
%A Kathleen Scott
%A Nagiza F Samatova
%J BMC Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1752-0509-6-40
%X 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.We formulate a problem¡ªenumeration of phenotype-determining system component interplays¡ªand 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.Dynamic biological systems, such as cells, are inherently complex. This complexity arises from the selective and nonlinear interconnections of functionally diverse system components to produce coherent behavior. The k
%U http://www.biomedcentral.com/1752-0509/6/40