Cargando…
Spice: discovery of phenotype-determining component interplays
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...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3515406/ https://www.ncbi.nlm.nih.gov/pubmed/22583800 http://dx.doi.org/10.1186/1752-0509-6-40 |
_version_ | 1782252172910526464 |
---|---|
author | Chen, Zhengzhang Padmanabhan, Kanchana Rocha, Andrea M Shpanskaya, Yekaterina Mihelcic, James R Scott, Kathleen Samatova, Nagiza F |
author_facet | Chen, Zhengzhang Padmanabhan, Kanchana Rocha, Andrea M Shpanskaya, Yekaterina Mihelcic, James R Scott, Kathleen Samatova, Nagiza F |
author_sort | Chen, Zhengzhang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3515406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35154062012-12-06 Spice: discovery of phenotype-determining component interplays Chen, Zhengzhang Padmanabhan, Kanchana Rocha, Andrea M Shpanskaya, Yekaterina Mihelcic, James R Scott, Kathleen Samatova, Nagiza F BMC Syst Biol Research Article 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. BioMed Central 2012-05-14 /pmc/articles/PMC3515406/ /pubmed/22583800 http://dx.doi.org/10.1186/1752-0509-6-40 Text en Copyright ©2012 Chen et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Chen, Zhengzhang Padmanabhan, Kanchana Rocha, Andrea M Shpanskaya, Yekaterina Mihelcic, James R Scott, Kathleen Samatova, Nagiza F Spice: discovery of phenotype-determining component interplays |
title | Spice: discovery of phenotype-determining component interplays |
title_full | Spice: discovery of phenotype-determining component interplays |
title_fullStr | Spice: discovery of phenotype-determining component interplays |
title_full_unstemmed | Spice: discovery of phenotype-determining component interplays |
title_short | Spice: discovery of phenotype-determining component interplays |
title_sort | spice: discovery of phenotype-determining component interplays |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3515406/ https://www.ncbi.nlm.nih.gov/pubmed/22583800 http://dx.doi.org/10.1186/1752-0509-6-40 |
work_keys_str_mv | AT chenzhengzhang spicediscoveryofphenotypedeterminingcomponentinterplays AT padmanabhankanchana spicediscoveryofphenotypedeterminingcomponentinterplays AT rochaandream spicediscoveryofphenotypedeterminingcomponentinterplays AT shpanskayayekaterina spicediscoveryofphenotypedeterminingcomponentinterplays AT mihelcicjamesr spicediscoveryofphenotypedeterminingcomponentinterplays AT scottkathleen spicediscoveryofphenotypedeterminingcomponentinterplays AT samatovanagizaf spicediscoveryofphenotypedeterminingcomponentinterplays |