Analysis of mass spectral serum profiles for biomarker selection.
Bioinformatics
; 21(21): 4039-45, 2005 Nov 01.
Article
em En
| MEDLINE
| ID: mdl-16159919
ABSTRACT
MOTIVATION Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection. RESULTS:
The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum.AVAILABILITY:
MATLAB scripts to implement the methods described in this paper are available from the HWR's lab website http//lombardi.georgetown.edu/labpage
Buscar no Google
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise Química do Sangue
/
Algoritmos
/
Mapeamento de Peptídeos
/
Biomarcadores Tumorais
/
Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz
/
Neoplasias Hepáticas
/
Proteínas de Neoplasias
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2005
Tipo de documento:
Article
País de afiliação:
Estados Unidos