Introduction to algorithms
The weighted majority algorithm
Information and Computation
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Prediction games and arcing algorithms
Neural Computation
Machine Learning
Machine Learning
Potential-Based Algorithms in On-Line Prediction and Game Theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Modern Applied Statistics with S
Modern Applied Statistics with S
Efficient model-based clustering for LC-MS data
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Detecting novel hypermethylated genes in Breast cancer benefiting from feature selection
Computers in Biology and Medicine
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We have developed an integrated tool for statistical analysis of large-scale LC-MS profiles of complex protein mixtures comprising a set of procedures for data processing, selection of biomarkers used in early diagnostic and classification of patients based on their peptide mass fingerprints. Here, a novel boosting technique is proposed, which is embedded in our framework for MS data analysis. Our boosting scheme is based on Hannan-consistent game playing strategies. We analyze boosting from a game-theoretic perspective and define a new class of boosting algorithms called H-boosting methods. In the experimental part of this work we apply the new classifier together with classical and state-of-the-art algorithms to classify ovarian cancer and cystic fibrosis patients based on peptide mass spectra. The methods developed here provide automatic, general, and efficient means for processing of large scale LC-MS datasets. Good classification results suggest that our approach is able to uncover valuable information to support medical diagnosis.