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The FAST-AIMS Clinical Mass Spectrometry Analysis System

Within clinical proteomics, mass spectrometry analysis of biological samples is emerging as an important high-throughput technology, capable of producing powerful diagnostic and prognostic models and identifying important disease biomarkers. As interest in this area grows, and the number of such pro...

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Detalles Bibliográficos
Autores principales: Fananapazir, Nafeh, Statnikov, Alexander, Aliferis, Constantin F.
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775698/
https://www.ncbi.nlm.nih.gov/pubmed/19956420
http://dx.doi.org/10.1155/2009/598241
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author Fananapazir, Nafeh
Statnikov, Alexander
Aliferis, Constantin F.
author_facet Fananapazir, Nafeh
Statnikov, Alexander
Aliferis, Constantin F.
author_sort Fananapazir, Nafeh
collection PubMed
description Within clinical proteomics, mass spectrometry analysis of biological samples is emerging as an important high-throughput technology, capable of producing powerful diagnostic and prognostic models and identifying important disease biomarkers. As interest in this area grows, and the number of such proteomics datasets continues to increase, the need has developed for efficient, comprehensive, reproducible methods of mass spectrometry data analysis by both experts and nonexperts. We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data preprocessing, feature selection, classification model generation, and performance estimation. FAST-AIMS is an efficient and user-friendly stand-alone software for predictive analysis of mass spectrometry data. The present resource review paper will describe the features and use of the FAST-AIMS system. The system is freely available for download for noncommercial use.
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spelling pubmed-27756982009-12-02 The FAST-AIMS Clinical Mass Spectrometry Analysis System Fananapazir, Nafeh Statnikov, Alexander Aliferis, Constantin F. Adv Bioinformatics Resource Review Within clinical proteomics, mass spectrometry analysis of biological samples is emerging as an important high-throughput technology, capable of producing powerful diagnostic and prognostic models and identifying important disease biomarkers. As interest in this area grows, and the number of such proteomics datasets continues to increase, the need has developed for efficient, comprehensive, reproducible methods of mass spectrometry data analysis by both experts and nonexperts. We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data preprocessing, feature selection, classification model generation, and performance estimation. FAST-AIMS is an efficient and user-friendly stand-alone software for predictive analysis of mass spectrometry data. The present resource review paper will describe the features and use of the FAST-AIMS system. The system is freely available for download for noncommercial use. Hindawi Publishing Corporation 2009 2009-07-09 /pmc/articles/PMC2775698/ /pubmed/19956420 http://dx.doi.org/10.1155/2009/598241 Text en Copyright © 2009 Nafeh Fananapazir et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Resource Review
Fananapazir, Nafeh
Statnikov, Alexander
Aliferis, Constantin F.
The FAST-AIMS Clinical Mass Spectrometry Analysis System
title The FAST-AIMS Clinical Mass Spectrometry Analysis System
title_full The FAST-AIMS Clinical Mass Spectrometry Analysis System
title_fullStr The FAST-AIMS Clinical Mass Spectrometry Analysis System
title_full_unstemmed The FAST-AIMS Clinical Mass Spectrometry Analysis System
title_short The FAST-AIMS Clinical Mass Spectrometry Analysis System
title_sort fast-aims clinical mass spectrometry analysis system
topic Resource Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775698/
https://www.ncbi.nlm.nih.gov/pubmed/19956420
http://dx.doi.org/10.1155/2009/598241
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