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Lukas
Lukas Käll
Associate Professor
Bioinformatics, Proteomics, Statistics

Description

Modern biology is to an increasing degree dependent on so called high-throughput techniques, i.e. massively parallel experiments that generate a large set of readouts. Examples of such techniques are shotgun proteomics and massively parallel sequencing. A common challenge for these kinds of experiments is that the interpretation of the outcomes, as the individual measurements are of varying quality. We are aiming at increasing the yield and facilitating the interpretation of high-throughput experiments by using different machine learning methods such as support vector machines and dynamical Bayesian networks.

Communities

Publications

YearTitle
2014 Crux : Rapid Open Source Protein Tandem Mass Spectrometry Analysis
2014 Engagera och aktivera studenter med inspiration från konferenser : examination genom poster-presentation
2014 Engagera och aktivera studenter med inspiration från konferenser: examination genom posterpresentation
2014 Fast and accurate database searches with MS-GF+percolator
2014 GradientOptimizer : An open-source graphical environment for calculating optimized gradients in reversed-phase liquid chromatography
2014 HiRIEF LC-MSMS enables deep proteome coverage and unbiased proteogenomics
2013 Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics
2013 Mass fingerprinting of complex mixtures : protein inference from high-resolution peptide masses and predicted retention times
2013 Nonparametric bayesian evaluation of differential protein quantification
2013 Optimized Nonlinear Gradients for Reversed-Phase Liquid Chromatography in Shotgun Proteomics
2012 Chromatographic retention time prediction for posttranslationally modified peptides
2012 A cross-validation scheme for machine learning algorithms in shotgun proteomics
2012 Enhanced peptide identification by electron transfer dissociation using an improved mascot percolator
2012 Recognizing Uncertainty Increases Robustness and Reproducibility of Mass Spectrometry-based Protein Inferences

Projects

Year Title
  Computational optimization of mass spectrometry-based proteomics experiments