Time-Series Alignment by Non-negative Multiple Generalized Canonical Correlation Analysis
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Adaptive bandwidth selection for biomarker discovery in mass spectrometry
Artificial Intelligence in Medicine
Optimal Transitions for Targeted Protein Quantification: Best Conditioned Submatrix Selection
COCOON '09 Proceedings of the 15th Annual International Conference on Computing and Combinatorics
A fast and accurate algorithm for the quantification of peptides from mass spectrometry data
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Profile-Based LC-MS Data Alignment--A Bayesian Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Mass spectrometry (MS) combined with high-performance liquid chromatography (LC) has received considerable attention for high-throughput analysis of proteomes. Isotopic labeling techniques such as ICAT [5,6] have been successfully applied to derive differential quantitative information for two protein samples, however at the price of significantly increased complexity of the experimental setup. To overcome these limitations, we consider a label-free setting where correspondences between elements of two samples have to be established prior to the comparative analysis. The alignment between samples is achieved by nonlinear robust ridge regression. The correspondence estimates are guided in a semi-supervised fashion by prior information which is derived from sequenced tandem mass spectra. Results: The semi-supervised method for finding correspondences was successfully applied to aligning highly complex protein samples, even if they exhibit large variations due to different biological conditions. A large-scale experiment clearly demonstrates that the proposed method bridges the gap between statistical data analysis and label-free quantitative differential proteomics. Availability: The software will be available on the website Contact: bernd.fischer@inf.ethz.ch