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
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. cancerpatients 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 cancerpatients 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
Authors: Maureen B Tracy; Haijian Chen; Dennis M Weaver; Dariya I Malyarenko; Maciek Sasinowski; Lisa H Cazares; Richard R Drake; O John Semmes; Eugene R Tracy; William E Cooke Journal: Proteomics Date: 2008-04 Impact factor: 3.984
Authors: Zhiqun Tang; Rency S Varghese; Slavka Bekesova; Christopher A Loffredo; Mohamed Abdul Hamid; Zuzana Kyselova; Yehia Mechref; Milos V Novotny; Radoslav Goldman; Habtom W Ressom Journal: J Proteome Res Date: 2010-01 Impact factor: 4.466
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