Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data

Detalles Bibliográficos
Autores principales: Rogers, Gary L, Moscato, Pablo, Langston, Michael A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Poster Presentation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3290075/
http://dx.doi.org/10.1186/1471-2105-11-S4-P21
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author Rogers, Gary L
Moscato, Pablo
Langston, Michael A
author_facet Rogers, Gary L
Moscato, Pablo
Langston, Michael A
author_sort Rogers, Gary L
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spelling pubmed-32900752012-03-01 Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data Rogers, Gary L Moscato, Pablo Langston, Michael A BMC Bioinformatics Poster Presentation BioMed Central 2010-07-23 /pmc/articles/PMC3290075/ http://dx.doi.org/10.1186/1471-2105-11-S4-P21 Text en Copyright ©2010 Rogers et al; licensee BioMed Central Ltd.
spellingShingle Poster Presentation
Rogers, Gary L
Moscato, Pablo
Langston, Michael A
Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data
title Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data
title_full Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data
title_fullStr Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data
title_full_unstemmed Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data
title_short Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data
title_sort graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data
topic Poster Presentation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3290075/
http://dx.doi.org/10.1186/1471-2105-11-S4-P21
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AT moscatopablo graphalgorithmsformachinelearningacasecontrolstudybasedonprostatecancerpopulationsandhighthroughputtranscriptomicdata
AT langstonmichaela graphalgorithmsformachinelearningacasecontrolstudybasedonprostatecancerpopulationsandhighthroughputtranscriptomicdata

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