Cargando…

Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster

BACKGROUND: Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of micr...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Wensheng, Zou, Sige, Song, Jiuzhou
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2322984/
https://www.ncbi.nlm.nih.gov/pubmed/18307794
http://dx.doi.org/10.1186/1471-2105-9-129
_version_ 1782152608049266688
author Zhang, Wensheng
Zou, Sige
Song, Jiuzhou
author_facet Zhang, Wensheng
Zou, Sige
Song, Jiuzhou
author_sort Zhang, Wensheng
collection PubMed
description BACKGROUND: Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of microarray data represents a valuable source for generating functional hypotheses of uncharacterized genes. RESULTS: This study focused on using support vector machines (SVM) to predict GO biological processes from individual or multiple-tissue transcriptional profiles of aging in Drosophila melanogaster. Ten-fold cross validation was implemented to evaluate the prediction. One-tail Fisher's exact test was conducted on each cross validation and multiple testing was addressed using BH FDR procedure. The results showed that, of the 148 pursued GO biological processes, fifteen terms each had at least one model with FDR-adjusted p-value (Adj.p) <0.05 and six had the values between 0.05 and 0.25. Furthermore, all these models had the prediction sensitivity (SN) over 30% and specificity (SP) over 80%. CONCLUSION: We proposed the concept of term-tissue specific models indicating the fact that the major part of the optimized prediction models was trained from individual tissue data. Furthermore, we observed that the memberships of the genes involved in all the three pursued children biological processes on mitochondrial electron transport could be predicted from the transcriptional profiles of aging (Adj.p < 0.01). This finding may be important in biology because the genes of mitochondria play a critical role in the longevity of C. elegans and D. melanogaster.
format Text
id pubmed-2322984
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-23229842008-04-18 Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster Zhang, Wensheng Zou, Sige Song, Jiuzhou BMC Bioinformatics Research Article BACKGROUND: Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of microarray data represents a valuable source for generating functional hypotheses of uncharacterized genes. RESULTS: This study focused on using support vector machines (SVM) to predict GO biological processes from individual or multiple-tissue transcriptional profiles of aging in Drosophila melanogaster. Ten-fold cross validation was implemented to evaluate the prediction. One-tail Fisher's exact test was conducted on each cross validation and multiple testing was addressed using BH FDR procedure. The results showed that, of the 148 pursued GO biological processes, fifteen terms each had at least one model with FDR-adjusted p-value (Adj.p) <0.05 and six had the values between 0.05 and 0.25. Furthermore, all these models had the prediction sensitivity (SN) over 30% and specificity (SP) over 80%. CONCLUSION: We proposed the concept of term-tissue specific models indicating the fact that the major part of the optimized prediction models was trained from individual tissue data. Furthermore, we observed that the memberships of the genes involved in all the three pursued children biological processes on mitochondrial electron transport could be predicted from the transcriptional profiles of aging (Adj.p < 0.01). This finding may be important in biology because the genes of mitochondria play a critical role in the longevity of C. elegans and D. melanogaster. BioMed Central 2008-02-28 /pmc/articles/PMC2322984/ /pubmed/18307794 http://dx.doi.org/10.1186/1471-2105-9-129 Text en Copyright © 2008 Zhang 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
Zhang, Wensheng
Zou, Sige
Song, Jiuzhou
Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster
title Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster
title_full Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster
title_fullStr Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster
title_full_unstemmed Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster
title_short Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster
title_sort term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2322984/
https://www.ncbi.nlm.nih.gov/pubmed/18307794
http://dx.doi.org/10.1186/1471-2105-9-129
work_keys_str_mv AT zhangwensheng termtissuespecificmodelsforpredictionofgeneontologybiologicalprocessesusingtranscriptionalprofilesofagingindrosophilamelanogaster
AT zousige termtissuespecificmodelsforpredictionofgeneontologybiologicalprocessesusingtranscriptionalprofilesofagingindrosophilamelanogaster
AT songjiuzhou termtissuespecificmodelsforpredictionofgeneontologybiologicalprocessesusingtranscriptionalprofilesofagingindrosophilamelanogaster