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A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

BACKGROUND: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-cons...

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Autores principales: Costa, Pedro R, Acencio, Marcio L, Lemke, Ney
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045802/
https://www.ncbi.nlm.nih.gov/pubmed/21210975
http://dx.doi.org/10.1186/1471-2164-11-S5-S9
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author Costa, Pedro R
Acencio, Marcio L
Lemke, Ney
author_facet Costa, Pedro R
Acencio, Marcio L
Lemke, Ney
author_sort Costa, Pedro R
collection PubMed
description BACKGROUND: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products. RESULTS: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively. CONCLUSIONS: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.
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spelling pubmed-30458022011-03-01 A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data Costa, Pedro R Acencio, Marcio L Lemke, Ney BMC Genomics Proceedings BACKGROUND: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products. RESULTS: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively. CONCLUSIONS: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability. BioMed Central 2010-12-22 /pmc/articles/PMC3045802/ /pubmed/21210975 http://dx.doi.org/10.1186/1471-2164-11-S5-S9 Text en Copyright ©2010 Costa 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 Proceedings
Costa, Pedro R
Acencio, Marcio L
Lemke, Ney
A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
title A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
title_full A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
title_fullStr A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
title_full_unstemmed A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
title_short A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
title_sort machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045802/
https://www.ncbi.nlm.nih.gov/pubmed/21210975
http://dx.doi.org/10.1186/1471-2164-11-S5-S9
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