Sciweavers

Share
GECCO
2005
Springer

Particle swarm optimization for analysis of mass spectral serum profiles

9 years 9 months ago
Particle swarm optimization for analysis of mass spectral serum profiles
Serum profiling using mass spectrometry is an emerging technology with a great potential to provide biomarkers for complex diseases such as cancer. However, protein profiles obtained from current mass spectrometric technologies are characterized by their high dimensionality and complex spectra with substantial level of noise. These characteristics have generated challenges in discovery of proteins and protein-profiles that distinguish cancer patients from healthy individuals. This paper proposes a novel machine learning method that combines support vector machines with particle swarm optimization for biomarker discovery. Prior to applying the proposed biomarker selection algorithm, low-level analysis methods are used for smoothing, baseline correction, normalization, and peak detection. The proposed method is applied for biomarker discovery from serum mass spectral profiles of liver cancer patients and controls. Categories and Subject Descriptors: I.5.2 [Pattern Recognition]: Design M...
Habtom W. Ressom, Rency S. Varghese, Daniel Saha,
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where GECCO
Authors Habtom W. Ressom, Rency S. Varghese, Daniel Saha, Eduard Orvisky, Lenka Goldman, Emanuel F. Petricoin, Thomas P. Conrads, Timothy D. Veenstra, Mohamed Abdel-Hamid, Christopher A. Loffredo, Radoslav Goldman
Comments (0)
books