Idioma:

Analysis of mass spectral serum profiles for biomarker selection

Ressom, Habtom W. ; Varghese, Rency S. ; Abdel-Hamid, Mohamed ; Eissa, Sohair Abdel-Latif ; Saha, Daniel ; Goldman, Lenka ; Petricoin, Emanuel F. ; Conrads, Thomas P. ; Veenstra, Timothy D. ; Loffredo, Christopher A. ; Goldman, Radoslav

Bioinformatics, 2005-11, Vol.21 (21), p.4039-4045 [Periódico revisado por pares]

Oxford: Oxford University Press

Texto completo disponível

Citações Citado por

Enviar para

Título:
Analysis of mass spectral serum profiles for biomarker selection
Autor: Ressom, Habtom W. ; Varghese, Rency S. ; Abdel-Hamid, Mohamed ; Eissa, Sohair Abdel-Latif ; Saha, Daniel ; Goldman, Lenka ; Petricoin, Emanuel F. ; Conrads, Thomas P. ; Veenstra, Timothy D. ; Loffredo, Christopher A. ; Goldman, Radoslav
Assuntos: Algorithms ; Artificial Intelligence ; Biological and medical sciences ; Biomarkers, Tumor - blood ; Blood Chemical Analysis - methods ; Diagnosis, Computer-Assisted - methods ; Fundamental and applied biological sciences. Psychology ; Gene Expression Profiling - methods ; General aspects ; Humans ; Liver Neoplasms - blood ; Liver Neoplasms - diagnosis ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Neoplasm Proteins - blood ; Peptide Mapping - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods
É parte de: Bioinformatics, 2005-11, Vol.21 (21), p.4039-4045
Notas: local:bti670
To whom correspondence should be addressed.
ark:/67375/HXZ-4VMDHMGS-S
istex:77395B1B03830023CB43A6EE09656A1314716B72
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
Descrição: 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 Contact: hwr@georgetown.edu
Editor: Oxford: Oxford University Press
Idioma: Inglês

Links

Voltar para lista de resultados

Anterior Resultado 4 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

Analysis of mass spectral serum profiles for biomarker selection

Ressom, Habtom W. ; Varghese, Rency S. ; Abdel-Hamid, Mohamed ; Eissa, Sohair Abdel-Latif ; Saha, Daniel ; Goldman, Lenka ; Petricoin, Emanuel F. ; Conrads, Thomas P. ; Veenstra, Timothy D. ; Loffredo, Christopher A. ; Goldman, Radoslav

Oxford: Oxford University Press

Buscando em bases de dados remotas. Favor aguardar.