The nature of statistical learning theory
The nature of statistical learning theory
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Machine Learning Approach to Mass Spectra Classification with Unsupervised Feature Selection
Computational Intelligence Methods for Bioinformatics and Biostatistics
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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.